Temporal Variation of Inflow to the Dams of Rajasthan State ...

i

Temporal Variation of Inflow to the Dams of

Rajasthan State with Special Reference to

Ramgarh and Bisalpur Dams

This thesis is submitted as a partial fulfilment of the Ph.D. programme in

Engineering

Naveen Kumar Gupta

ID No.: 2011RCE7135

Department of Civil Engineering

MALAVIYA NATIONAL INSTITUTE OF TECHNOLOGY

JAIPUR-302017

July, 2016

ii

MALAVIYA NATIONAL INSTITUTE OF TECHNOLOGY,

JAIPUR-302017

CERTIFICATE

This is to certify that the thesis report entitled “Temporal Variation of Inflow to the

Dams of Rajasthan State with Special Reference to Ramgarh and Bisalpur Dams”

which is being submitted by Naveen Kumar Gupta, ID No.: 2011RCE7135, for the

partial fulfillment of the degree of Doctor of Philosophy in Civil Engineering in the

Malaviya National Institute of Technology, Jaipur has been carried out by him under

my supervision and guidance.

Date: July 8, 2016

Place: Jaipur

(Dr. Ajay Singh Jethoo)

Supervisor

Associate Professor, Civil Engineering Department

Malaviya National Institute of Technology

Jaipur- 302017

Rajasthan

India

iii

MALAVIYA NATIONAL INSTITUTE OF TECHNOLOGY,

JAIPUR-302017

CANDIDATE’S DECLARATION

I hereby certify that the work which is being presented in the thesis entitled

„Temporal Variation of Inflow to the Dams of Rajasthan State with Special

Reference to Ramgarh and Bisalpur Dams’ in partial fulfilment of the requirements

for the award of the Degree of Doctor of Philosophy and submitted in the Department

of Civil Engineering, Malaviya National Institute of Technology Jaipur, is an

authentic record of my own work carried out at Department of Civil Engineering

during a period from December 27, 2011 to July 08, 2016 under the supervision of Dr.

Ajay Singh Jethoo, Associate Professor of Civil Engineering Department, Malaviya

National Institute of Technology Jaipur.

The matter presented in this thesis has not been submitted by me for the award of any

other degree of this or any other Institute.

Dated: July 8, 2016 (Naveen Kumar Gupta)

Place: Jaipur ID No.: 2011RCE7135

This is to certify that the above statement made by the candidate is true to the best of

my knowledge.

Date: July 8, 2016

Place: Jaipur

(Dr. Ajay Singh Jethoo)

Supervisor

Associate Professor, Civil Engineering Department

Malaviya National Institute of Technology

Jaipur- 302017

Rajasthan

India

iv

Acknowledgement

The present work for all its outcomes owes a lot to a large number of people. First and

foremost being Dr. Ajay Singh Jethoo, Associate Professor, Department of Civil

Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India my

guide and supervisor to whom I owe gratitude for his valuable guidance without

which this research work would not have been a success. Apart from being my

supervisor, he was always there when I needed him. He taught me that for going

deeper into the field, I must start playing with things and must not stop till the last

stage is conquered. His perceptual inspiration, encouragement, and understanding

have been a mainstay of this work. From his busy schedule, he always spared time for

assessing the progress of my work. His wide knowledge regarding the subject helped

me in writing this thesis. I am indebted to Dr. Sandeep Choudhary, Associate

Professor, Department of Civil Engineering, for his kind help and support which made

it possible for me to stand up to the challenges offered by the task and come out

successfully. I thank him sincerely for his inspiration and guidance he showered on

me.

I extend my deep sense of gratitude to Prof. A.B. Gupta, Director and Prof.

I.K. Bhatt, Ex-Director, MNIT, Jaipur for strengthening the research environment of

the Institute by providing all necessary facilities. I am thankful to Prof. Ravindra

Nagar Dean (Academics), Officers, and Staff of academic affairs for their cooperation

in academic work and help throughout the course of study. I am not able to find words

in any of the dictionaries for thanking Prof. Gunwant Sharma, Prof. Y.P. Mathur,

Prof. A.K.Vyas, Prof. Sudhir Kumar, Dr. Mahendra Choudahry and Dr. Urmila

Brighu, Department of Civil Engineering, MNIT, Jaipur for their valuable guidance,

unfailing encouragement, keeping my moral high during the course of the work and

helped me out whenever I needed them. I am extremely thankful to members of

DPGC and DREC, for their support and guidelines regarding my thesis work. I also

pay my sincere regards to Prof. K.C. Jain for helping me out in the analysis of my

thesis results.

I deeply acknowledge Er. B.K. Gupta, Chief Engineer, PHED (Retd.); Er.

Pradeep Mathur, Chief Engineer (Retd.); Er. Raja Ram Yadav, Chief Engineer

(Retd.); Er. Vinay Chandwani Executive Engineer; Er. Sunil Vyas, Executive

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Engineer; Er. Kiran Ahuja, Executive Engineer; Er. N.P. Singh, Executive Engineer;

Er. Dharmesh Yadav, Executive Engineer and all other engineers and staff of Water

Resources Department, Government of Rajasthan, India, whose regular support,

guidance, motivation, and contribution towards my thesis cannot be unseen.

I am also thankful to Dr. S.K Tiwari, Dr. Sumit Khandelwal, Dr. Nivedita

Kaul, Dr. Mahesh Jat, Dr. Pawan Kalla, Dr. Arun Gaur, Prof. Rohit Goyal, Prof. B.L.

Swami, Dr. M.K. Shrimali, Dr. S.D. Bharti, Dr. J.K. Jain, Dr. Rajesh Gupta, Dr.

Sanjay Bhatter and all other faculty members of Malaviya National Institute of

Technology Jaipur.

I am grateful to the Head, Civil Engineering Department, and Dean R&D for

providing me with the environment and space to carry out my research work.

I give my thanks to Mr. Rajesh Saxena, Office-in-Charge, Civil Engineering

Department, MNIT Jaipur, Mr. Ramjilal Meena and Mr. Sher Singh, who were

always ready to extend me every possible help throughout my work.

From the bottom of my heart, I ever realize the inspiration given by my father,

Late Mr. Subhash Chand Gupta and mother, Mrs. Shanti Devi. The most valued

efforts and guidance from my brother, Mr. Amolak Khandelwal, Chartered

Accountant, provided me with the urge of doing the best work. I cannot forget to

acknowledge here the tolerance and patience of my kids, Mansi and Umang, which

they kept during the progress of my research work. Lastly but not the least, I would

like to acknowledge the efforts made by my wife, Mrs. Poonam Gupta, in providing

me with the environment to carry on my thesis work at home with ease. I would like

to thank Almighty God, without whose blessings, it was not possible to even think

about doing this work.

Finally, I would like to thank all those people who helped, guided, and

supported me in my study. Thank you all!

Dated: July 8, 2016 (Naveen Kumar Gupta)

Place: Jaipur ID No.: 2011RCE7135

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Abstract

The study aims to investigate the temporal variation of inflow to the existing dams of

Rajasthan State especially related to Ramgarh and Bisalpur dams. Inflows to these

dams in Rajasthan have been decreasing in recent decades. Population in the state is

regularly increasing. Because of droughts, overuse of surface and subsurface water

resources, construction of small water harvesting structures and changes in land use

and other anthropogenic impacts, water levels have decreased tremendously. The

attribution of inflow variability to anthropogenic activities is a challenging problem

and an active research area.

Rajasthan is a semi-arid state with the highest of the geographical area of the

Indian subcontinent. Rainfall variation is very high over the state, ranging from 190

mm to 1000 mm from west to south/east. Average rainfall over the state is 575 mm

against the national average of 1182 mm. Matters of water resources planning are of

prime importance for the state. There are 243 blocks, out of which 172 blocks are

overexploited. Only 25 blocks, i.e. only 10% blocks in the state may be considered

safe for groundwater withdrawal. The average inflow to the surface water reservoirs is

around 39% only. It has been observed from the storage data of the dams of the

Rajasthan State that there are temporal variations in inflows. Reduced inflows to the

dams are creating a water stress condition in the state. Detailed study of Ramgarh and

Bisalpur dams has been taken in this research because these two dams are related to

the drinking water supply need of the capital city Jaipur.

Methodology to conduct the study includes the formation of the null

hypothesis to test the observed dependabilities and inflows of the existing dams of

various river basins. Results of t-test and Chi-square test infer the rejection of the null

hypothesis, i.e. dependabilities have been changed, and inflow to the dams is not as

per their expected standards. Simulated inflows have been computed with the help of

Thiessen polygon and Strange‟s table, as per the prevailing practices of Water

Resources Department of Government of Rajasthan. Performance statistics and Nash-

Sutcliff Efficiency results infer that the observed mean values are better indicators

than the simulated values. The analysis of rainfall, simulated yield and observed

inflow showed that the inflow pattern is decreasing even after a slightly increasing

pattern of rainfall. Time series analysis and sequential cluster analysis have been done

vii

to find out the critical year, which divides two consecutive non-overlapping epochs‟

namely, pre-disturbance and post-disturbance.

The study revealed that 1994-1998 were critical years. Correlation matrix has

been made to assess the mutual correlation of the factors with each other. The work is

extended to determine the main factors, which reduce the inflow to the existing dams.

Results of correlation and regression analysis along with Cosine Amplitude Method

(CAM) showed the significance of land use changes over the inflow. Land use has a

significant correlation with population density. The same analysis has been conducted

for Rajasthan state as a whole and Ramgarh and Bisalpur dams specifically.

The study has highlighted that in spite of an increasing trend in rainfall

witnessed during the last 113 years, the inflow to the dams is decreasing at a fast pace

owing to a decrease in the percentage area contributing to surface runoff. In these

circumstances, it becomes pertinent to plan conjunctively the storage and uses of the

surface as well as subsurface water in the state of Rajasthan along with maintaining

the ecological sustainability.

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TABLE OF CONTENTS

Inner Title Page i

Certificate ii-iii

Acknowledgement iv-v

Abstract vi-vii

Table of Contents viii-xiii

List of Tables xiv-xv

List of Figures xvi-xviii

List of Appendices xix

List of Abbreviations xx-xxii

S.N. CONTENTS PAGE NO.

1 INTRODUCTION 1-13

1.1 General 2-5

1.2 Study Area 6-10

1.2.1 Rajasthan 6-8

1.2.2 Ramgarh Dam 8-9

1.2.3 Bisalpur Dam 9-10

1.3 Need of the Study 10-11

1.4 Objective of the Study 11-12

1.5 Organization of Thesis 12-13

2 LITERATURE REVIEW 14-57

2.1 Introduction 16-18

2.2 Data Period and Data Collection 18-20

2.3 Runoff and Inflow Variability to the Dams 20-25

2.4 RainfallVariability and Trends 25-27

2.5 Climatic Factors and Climate Changes 27-28

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2.6 Human Interventions and LULC changes 28-30

2.7 Groundwater Recharge/Depletion 30-32

2.8 Population Growth and Economic Expansion 32-33

2.9 Critical Year and Sensitive Analysis 34

2.10 Environmental Flow and Ecological Sustainability 34-36

2.11 Water Resources Planning and Management 36-53

2.11.1 Conjunctive Use of Surface and Subsurface

Water

40-41

2.11.2 River Basin Management 41-42

2.11.3 Small WHS and Large Dams 42-44

2.11.4 Micro Watershed Planning 44

2.11.5 IWRM and INRM 44-45

2.11.6 Transboundary Water Transfer and Water

Sharing

45-48

2.11.7 Water Pricing, Water Market, Water Trading,

and Water Privatization

48-50

2.11.8 Rain Water Harvesting 50-51

2.11.9 Reuse and Recycle of Waste Water 51-52

2.11.10 Water Balance 52-53

2.11.11 Water Use Efficiency 53

2.12 Summary 53-57

3 MATERIALS AND METHODS 58-80

3.1 Introduction 59

3.2 Materials: Data Collection 59-71

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3.2.1 Inflow Data 60-61

3.2.2 Rainfall Data 61-64

3.2.3 Climate Data 64

3.2.4 Land Use Land Cover Data 64-69

3.2.5 Groundwater Levels 70-71

3.2.6 Population Data 71

3.3 Methodology 72-78

3.3.1 Sampling 72

3.3.2 Inflow Trend Analysis 72-73

3.3.3 Formulation and Testing of Hypothesis 73

3.3.4 Performance Measures and Performance

Statistics

74-76

3.3.5 Determination of Critical Year 76

3.3.6 Various Factors, their Trend Analysis and

Correlation Matrix

76

3.3.7 Regression Analysis and Determination of

Principal Components

76-77

3.3.8 Cosine Amplitude Method of Sensitivity

Analysis and Determination of Principal

Components

77-78

3.3.9 Multiple Linear Regression (MLR) for

Generation of Inflow Equation

78

3.3.10 Regression Analysis and Determination of

Factors Responsible for Principal Components

78

3.4 Methodology Flow Chart 79

3.5 Underlying Assumptions 80

xi

4 TEMPORAL VARIATION OF INFLOW TO THE DAMS

OF RAJASTHAN STATE

81-115

4.1 Formulation and Testing of Hypothesis 85-86

4.2

Performance Study and Performance Statistics 86-89

4.3 Determination of Critical Year 90-92

4.3.1 Determination of Possible Critical Years: Time

Series Analysis

90-91

4.3.2 Determination of Critical Year: Sequential

Cluster Analysis

91-92

4.4 Various Parameters and Their Trends 92-110

4.4.1 Rainfall and other Climatic Parameters 92-98

4.4.2 Land Use Land Cover Changes 98-101

4.4.3 Groundwater Level Changes 101-106

4.4.4 Population Growth and Trends 106-108

4.4.5 Various Factors, Trend Lines and their Rate of

Changes

108-109

4.4.6 Correlation Matrix 109-110

4.5 Determination of Principal Components 111-115

4.5.1 Regression Analysis 111-114

4.5.2 Cosine Amplitude Method 115

5 TEMPORAL VARIATION OF INFLOW TO THE

RAMGARH DAM

116-144

5.1 Formulation and Testing of Hypothesis 120

5.2 Performance Study and Performance Statistics 120-122


xii


Series Analysis

122-123


Cluster Analysis

123-125





5.4.4 Population Growth 134-135

5.4.5 Various Parameters, Trend Lines and their Rate

of Changes

135-136




5.5.2 Cosine Amplitude Method 141-142

5.6 MLR for Generation of Inflow Equation 142-143

5.7 Factors Responsible for Principal Components 143-144


BISALPUR DAM

145-172

6.1 Formulation and Testing of Hypothesis 149-150

6.2 Performance Study and Performance Statistics 151-152



Series Analysis

152-154

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Cluster Analysis

154-156





6.4.4 Population Growth 162-163


of Changes

163-164




6.5.2 Cosine Amplitude Method 169

6.6 MLR for Generation of Inflow Equation 170-171

6.7 Regression Analysis and Determination of Factors

Responsible for Principal Components

171-172

7 CONCLUSIONS AND RECOMMENDATIONS 173-179

7.1 Conclusions 174-175

7.2 Recommendations 176-177

7.3 Major Contributions of Present Study 177-178

7.4 Limitations of Findings 178-179

7.5 Scope of Further Research 179

REFERENCES 180-192

APPENDICES 193-218

AUTHOR’S BIO-DATA 219

RESEARCH PAPERS PUBLISHED/UNDER REVIEW 219-220

xiv

LIST OF TABLES

Table No. Particular Page No.

Table 1.1 State parameters in comparison to the country 6

Table 1.2 Details of basin wise dams in Rajasthan state 8

Table 1.3 Salient features of Ramgarh dam 9

Table 1.4 Salient features of Bisalpur dam 10

Table 1.5 Drinking water requirements from Bisalpur dam (MCM) 10

Table 1.6 Summary detail of study area 10

Table 2.1 Classification of intensity of rainfall 26

Table 2.2 Spatial distribution of weather phenomenon (percentage

area covered)

26

Table 3.1 Surface water infrastructure of the state 60

Table 3.2 Computation of influence factor: Ramgarh 61

Table 3.3 Land utilization of the Rajasthan state (000 ha) 65-66

Table 3.4 Trend of contributing and reducing effect of land use of

Rajasthan state on runoff (000 sqkm)

67-68

Table 3.5 Variation of operational land holdings in the state 68

Table 3.6 Land utilization of the Ramgarh dam catchment (%) 69

Table 3.7 Land utilization of the Bisalpur dam catchment (%) 69

Table 3.8 Year wise average depth to water table below ground

level of the state

70-71

Table 3.9 Population data 71

Table 4.1 Year wise inflow (%) to the dams of Rajasthan state 83

Table 4.2 Testing of hypothesis (Basin wise): Chi-Square test 86

Table 4.3 Performance study of the dams of Tonk district 88-89

Table 4.4 Performance statistics for the dams of Tonk district 89

Table 4.5 Time series analysis: Dams of Rajasthan state 90

Table 4.6 Sequential cluster analysis: Dams of Rajasthan state 91

xv

Table 4.7 Scenario of groundwater development: Rajasthan 105

Table 4.8 Various parameters, trend lines and their rate of change 108

Table 4.9 Correlation matrix: Rajasthan 110

Table 4.10 Correlation and regression of inflow with various factors

(Rajasthan)

114

Table 5.1 Year wise inflow to the Ramgarh dam 118

Table 5.2 Performance Statistics: Ramgarh dam 121

Table 5.3 Time series analysis: Ramgarh dam (Trend elimination

using least square method)

122-123

Table 5.4 Sequential cluster analysis: Ramgarh dam 124

Table 5.5 Number of events for different rainfall intensities at four

RGS

127

Table 5.6 Factors and their trends: Ramgarh dam 136

Table 5.7 Correlation matrix: Ramgarh dam 137

Table 5.8 Correlation and regression of inflow with various

parameters: Ramgarh

140

Table 5.9 MLR summary output: Inflow to the Ramgarh dam 142

Table 6.1 Year wise inflow to the Bisalpur dam 147-148

Table 6.2 Performance statistics: Bisalpur dam 152

Table 6.3 Time series analysis: Bisalpur dam (Trend elimination

using least square method)

153

Table 6.4 Sequential cluster analysis: Bisalpur dam 155

Table 6.5 Factors and their trends: Bisalpur dam 163

Table 6.6 Correlation matrix: Bisalpur dam 165

Table 6.7 Correlation and regression of inflow with various

parameters: Bisalpur

168

Table 6.8 MLR summary output: Inflow to the Bisalpur dam 170

xvi

LIST OF FIGURES

Fig. No. Particular Page No.

Fig. 1.1 Declining trend of inflow even after good rainfall 4

Fig.1.2 Per capita water availability 5

Fig. 1.3 Study area 7

Fig. 2.1 Scheme of formation and aggravation of freshwater

deficiency

33

Fig. 3.1 Thiessen polygon for Ramgarh dam catchment 62

Fig. 3.2 Thiessen polygon for Bisalpur dam catchment 63

Fig. 3.3 Methodology flow chart 79

Fig. 4.1 Temporal variation of water inflow to the dams of

Rajasthan State

84

Fig. 4.2 Variation of inflow with dependability in the dams of

Rajasthan state

85

Fig. 4.3 Number of dams spillover in the state 85

Fig. 4.4 Rainfall trend and its distribution: Rajasthan 93-95

Fig. 4.5 Trends of other climatic factors: Rajasthan 97

Fig. 4.6 Land use (contributing effect): Rajasthan 98

Fig. 4.7 Land use changes (1957-2013): Rajasthan 99-100

Fig. 4.8 Development of agriculture and irrigation facilities in

Rajasthan

102

Fig. 4.9 Groundwater level changes: Rajasthan 103

Fig. 4.10 Scenario of groundwater development: Rajasthan 105

Fig. 4.11 Population and its growth: Rajasthan 106-107

Fig. 4.12 Cross plots showing the relationship between inflow (%)

and independent variables: Rajasthan

111-113

Fig. 4.13 Relative importance of the factors responsible for inflow

(Rajasthan): CAM

115

Fig. 5.1 Temporal variation of water inflow to the Ramgarh dam 117

xvii

Fig. 5.2 Water inflow trend for different time segment: Ramgarh 119

Fig. 5.3 Variation of inflow with dependability in Ramgarh dam 119

Fig. 5.4 Time series inflow of Ramgarh dam 123

Fig. 5.5 Rainfall trend: Ramgarh 125-126

Fig. 5.6 Trends of other climatic factors: Ramgarh 128-129

Fig. 5.7 Land use (contributing effect) and cultivation area trend:

Ramgarh

129

Fig. 5.8 Land use changes: Ramgarh 130-131

Fig. 5.9 Groundwater level changes: Ramgarh 132-134

Fig. 5.10 Population density and its growth: Ramgarh 135

Fig. 5.11 Cross plots: Between inflow and independent variables:

Ramgarh

138-139

Fig. 5.12 Relative influences of the factors responsible for inflow:

CAM for Ramgarh

141

Fig. 5.13 Goodness of fit for the developed equation (MLR):

Ramgarh

143

Fig. 6.1 Temporal variations of rainfall, theoretical yield, and

observed inflow to the Bisalpur dam

146

Fig. 6.2 Water inflow trend for different time segments: Bisalpur 149

Fig. 6.3 Variations of inflow with dependability in Bisalpur dam 151

Fig. 6.4 Time series inflow of Bisalpur dam 154

Fig. 6.5 Rainfall trend: Bisalpur 156-167

Fig. 6.6 Trends of other climatic factors: Bisalpur 158-159

Fig. 6.7 Land use (contributing effect): Bisalpur 159

Fig. 6.8 Land use changes: Bisalpur 159-160

Fig. 6.9 Pre-monsoon and Post-monsoon GWL below GL: Bisalpur 162

Fig. 6.10 Population density and its growth: Bisalpur 163

Fig. 6.11 Cross plots: Between inflow and independent variables:

Bisalpur

166-167

xviii

Fig. 6.12 Relative influence of the factors responsible for inflow:

CAM for Bisalpur dam

169

Fig. 6.13 Goodness of fit for the developed equation (MLR):

Bisalpur

171

Fig. 6.14 Correlation between factors responsible and principal

components

172

xix

LIST OF APPENDICES

Appendix No. Particular Page No.

Appendix-1 Detail of 115 major and medium dams of Rajasthan

State

193-196

Appendix-2 113-year rainfall data for ANOVA test (A3 Sheet

attached)

197

Appendix-3 Weather data of Ramgarh dam catchment 198

Appendix-4 Weather data of Bisalpur dam catchment 199

Appendix-5 Various input and output data: Rajasthan 200

Appendix-6 Various input and output data: Ramgarh 201-202

Appendix-7 Various input and output data: Bisalpur 203-204

Appendix-8a Cosine Amplitude Method (CAM): Rajasthan 205

Appendix-8b Cosine Amplitude Method (CAM): Ramgarh 206

Appendix-8c Cosine Amplitude Method (CAM): Bisalpur 207-208

Appendix-9 Weighted rainfall in the catchment of Ramgarh Dam 209

Appendix-10 Groundwater resources of Rajasthan state as on

31.03.2011

210

Appendix-10b Abstractions in the catchment area of existing dams

and resulting nearly nil inflow to these dams

211-216

Appendix-11 Some actual site photographs showing the changes in

LULC in the catchment areas of dams

217-218

xx

LIST OF ABBREVIATIONS

A 20-year data series

Ag. Agriculture

ANN Artificial Neural Network

ASMO Area Sown More than Once

Avg. Average

B 10-year data series

BCM Billion Cubic Metre

CAM Cosine Amplitude Method

Cum Cubic Metre

df Degree of Freedom

Dn Dependability

DNA Data Not Available

E.C.D. Earthen Check Dam

Ei Expected Weighted Dependability

ET Evapotranspiration

EWR Environmental Water Requirement

GIS Geographical Information System

GoR Government of Rajasthan

GPS Global Positioning System

GWD Groundwater Department

GWL Groundwater Level

GWL BGL Groundwater Level Below Ground Level

ha Hectare

hrs Hours

ICOLD International Commission On Large Dams

IF Influence Factor

xxi

IMD Indian Meteorological Department

INRM Integrated Natural Resources Management

IWRM Integrated Water Resources Management

LULC Land Use Land Cover

Max. Maximum

MCFT/mcft Million Cubic Feet

MCK Million Cubic Kilometre

MCM Million Cubic Metre

MEF Minimum Environmental Flow

mh Million Hectare

Min. Minimum

MLR Multiple Linear Regression

NA Not Applicable

NPS Non Point Source

NSE Nash-Sutcliffe Efficiency

Oi Observed Weighted Dependability

Q & Q Quality and Quantity

Resi. Residential

RGS Rain Gauge Station

RH Relative Humidity

Ri Relative Influence

R-R Rainfall-Runoff

RWH Rain Water Harvesting

sqkm Square Kilometre

Temp. Temperature

TMC Thousand Million Cubic Feet

xxii

U/S Upstream

WHS Water Harvesting Structure

WRD Water Resources Department

WT and WTRTBL Water Table

Wtd Weighted

Yr. Year

Study of Temporal Variation of Inflow to the Dams of Rajasthan state with Special Reference to Ramgarh and Bisalpur Dams.

1

1 INTRODUCTION

1.1 General

1.2 Study Area

1.2.1 Rajasthan

1.2.2 Ramgarh Dam

1.2.3 Bisalpur Dam

1.3 Need of the Study

1.4 Objective of the Study

1.5 Organization of Thesis


2

CHAPTER-1

INTRODUCTION

1.1 General

The importance of water for life was well explained by Mikhail Gorbachev (2000) as

cited by Draper (2008) as “Water, not unlike religion and ideology, has the power to

move millions of people. Since the very birth of human civilization, people have moved

to settle close to water. People move when there is too little of it; people move when there

it too much of it. People move on it. People write and sing and dance and dream about it.

People fight over it. And everybody, everywhere and every day, needs it. We need water

for drinking, for cooking, for washing, for food, for industry, for energy, for transport, for

rituals, for fun, for life. And it is not only we humans who need it; all life is dependent

upon water for its very survival”. Water stress or water scarcity is a burning problem

being faced by the present world. It is a threat to mankind, but a bigger threat to the

speechless creatures (Biodiversity i.e. animals and vegetations). The core issue is how

scarce water is allocated to meet the various demands (Liu et al. 2005; Draper 2007a; Han

et al. 2012). As per Human Development Report (UNDP 2006), as cited by Shah and

Kumar (2008), against the average consumption of 580 l water per person per day in US

and 500 l in the Australia, India gets 140 l only. Water demand is to be fulfilled keeping

in view the water available and minimum d/s flow. All three factors are largely dependent

upon precipitation, which is not under our control. Correct and dependable assessment of

the inflow to the dams is the first and primary step in the field of water management.

“Dams are modern temples of India”- Pt. Jawahar Lal Nehru quoted this statement

in 1952 and then a planned development of dams was taken up in India. Afterward

because of various reasons, many of the temples i.e. dams are waiting for their Lords i.e.

Water. Scientific research is required to answer the questions related to this issue. This

phenomenon is being observed in many river basins in the world, and several researchers

have studied this phenomenon (Liu et al. 2007; Wang et al. 2011; Hassanzadeh et al.

2012; Han et al. 2012). The effect of human activity on hydrology is becoming a point of

focus in the world with the developing society (Sang et al. 2010). Runoff in major rivers

in China has been decreasing in recent decades. The attribution to hydrologic variability


3

to human activity is a challenging problem and an active research area. Human activity

was the main driver behind 68% of the runoff reduction that occurred for the period of

1980 to 2008. A key aspect of anticipating and managing future variability is to

understand its causes. Such information would support the sustainable use of water

resources, but also inform efforts to maintain and restore natural ecosystem (Wang et al.

2011).

Temporal variations of inflow are being observed in many dams all over the

world, creating a crucial condition for drinking as well as agricultural water supply

besides the other demands of water (Fig. 1.1). Several field visits to the catchment areas

of the dams including Ramgarh and Bisalpur dams have been made during the last couple

of years. Drying up of wells and tube wells are showing lowering groundwater table.

Huge anthropogenic changes in the land use like increased cultivation and ploughing up

to the rock toe line, various form of encroachments and other infrastructural development,

construction of field bunds/field dams/weirs/check dams, etc. within the catchment areas

of the existing dams were observed. Drinking water supply of capital city Jaipur has been

suffered very much. Earlier the Ramgarh dam was being used for drinking water supply

to the city, but now it is completely dry even after a good rainfall. Now, Bisalpur dam is

being used for drinking water supply to the city, but it is also facing the same situation of

temporal variation of inflow. Therefore, it becomes essential to study the phenomenon of

temporal variation of inflow to the dams in the state. The issue of water resources

estimation and use has long been of particular scientific importance, but now it acquires

extremely acute social and political character. This is due, on the one hand, to the

increasing role of anthropogenic factors associated with water consumption by the

population, industry, and agriculture and, on the other hand, to changes in global and

regional climate (Shiklomanov et al. 2011). Piman and Babel (2012) explained the

importance of reduction of uncertainty in hydrologic predictions for water resources

development and management. The Ramgarh and Bisalpur dams are past and present

drinking water sources for Jaipur city respectively, therefore, are ideal sites to evaluate

the effects of changes in climate and human activities on the inflow to these dams.


4

(a) Hoover dam (b) Bhakra dam

(c) Ramgarh dam (d) Bisalpur dam

Fig. 1.1 Declining trend of inflow

The aim of this research study is to formulate and test the hypothesis for temporal

variation of inflow to the dams of the state. Time scale data 1990-2013, 1983-2014, and

1979-2013 are used for inflow analysis of dams of Rajasthan state, Ramgarh dam, and

Bisalpur dam respectively. Trends of rainfall, inflow, climatic factors, and land use

factors are to be analyzed; correlated and most significant factor responsible for declining

trend of inflow is to be determined. In the present study, an effort has been made to test

the inflow trend and to assess the reasons for declining trend which may be attributed to

some disturbances in rainfall pattern, catchment and climate characteristics. Sequential

Cluster analysis has been done to get the critical year and to show the pre-disturbance and

post-disturbance epochs. Correlation between water inflow and various factors has been

developed in this study and found that declining trend of water inflow in Ramgarh and

Bisalpur dams is attributed to various reasons like changes in land use land cover


5

(LULC), change in hydrogeological conditions, changes in climatic factors, and

indiscriminate infrastructure development. Population in the state is regularly increasing.

Ramgarh dam is situated near Jaipur city. Therefore, immigration of population in and

around the city is very fast. The land area is limited, but the population is regularly

increasing in and around the city. This situation is creating more and more value for every

piece of land. Firstly, divisions of land are making the operational land holding per

person very small and the secondly, the price hike is compelling every person to confine

his or her share of the land by making any type of boundary demarcation. Due to the

anthropogenic impacts and various types of human activities and infrastructure

development works which are taking place regularly to give room to every person in the

society, surface water availability is reducing. LULC is regularly changing at a very fast

pace, increasing the surface roughness thereby regularly decreasing the surface flow to

existing dams. Per capita water availability in the country is shown and compared in

Fig.1.2.

Fig. 1.2 Per capita water availability

The sample is selected through cluster sampling (Major and Medium dams in the

state); hypotheses have been framed and tested by applying the Chi-Square test and found

that there exist temporal variations of inflow to the dams of Rajasthan state. Detailed

study of Ramgarh and Bisalpur dams has been undertaken in this research because these

two dams are related to the drinking water supply need of the capital city Jaipur.

Temporal variations of inflow to Ramgarh and Bisalpur dams are also tested and found

5.3 4.6

10.6

19.6

2.3

14.9

2.5 2.4 4.0

8.7

2.1

9.9

1.5 1.8 1.8

4.2

2.0

7.7

0

2

4

6

8

10

12

14

16

18

20

India China Pakistan Nepal UK USA

00

0, m

3

Per capacity water availability

1955

1990

2025


6

that there exist declining trend of inflow to these dams. This is a descriptive type of

research describing the phenomenon in nature incorporating inferential statistics. Both

types of approach have been adopted to portray a complete picture of the problem.

1.2 Study Area

Study area comprises the Rajasthan state in general with special reference to Ramgarh

and Bisalpur dams.

1.2.1 Rajasthan State

The study area comprises the western part of India, with a geographical area of 3,43,000

km2, in a region of flat terrain that includes 15 different river basins, 33 districts, and

Ramgarh and Bisalpur dams (Fig. 1.3).

State parameters in comparison to the country parameters are shown in Table 1.1.

Western districts of the state are part of the Great Thar Desert. The study area always

faces the problems of runoff variability. There are no major and medium dams in four

river basins. Therefore, only 11 river basins are considered for testing the hypothesis.

Table 1.1 State parameters in comparison to the country

Parameter Country (India) State (Rajasthan) Percentage

(%)

Area (km2) 3,287,300 343,000 10.40

Population 2011 (million) 1210 68.50 5.66

Live stock (million) 292 54.7 18.70

Cultivable area (km2) 1,843,700 257,000 13.90

Food production (MT) 211 13.82 6.50

Average rainfall (mm) 1182 575 48.65

Per capita water availability (Cum) 1760 740 42.05

Total precipitation (BCM) 4000 197 4.90

Water availability (BCM) 1869 21.71 1.16

Utilizable water availability (BCM) 1121 16.05 1.43

Surface water utilization (BCM) 690 12.55 1.82

Replenishable groundwater (BCM) 431 8.13 1.89

Over-exploited blocks as on 2009 (nos.) 802 166 20.70

Desert blocks 142 85 60


7

Fig 1.3 Study area


8

There are 3302 minor, medium and major dams in the state. Total 2609 dams

having culturable command area up to 300 ha have been transferred to the „Gram

Panchayats‟ (Village Government). Remaining 693 dams are under the jurisdiction of

Water Resources Department of Government of Rajasthan state (Table 1.2). There are

214 dams classified in Large Dams as per definition of International Commission on

Large Dams (ICOLD). There are 22 major and 93 medium dams making a total of 115

major and medium dams in the state, which are used for drinking water supply schemes

for various districts and towns of the Rajasthan state. Detail of 115 dams is given in

Appendix-1.

Table 1.2 Details of basin wise dams in Rajasthan state

S.N. Name of

Basin

Dams transferred to villages

governments

Dams under Water

Resources Department Total Dams

Nos. Storage

(MCM)

CCA

(ha) Nos.

Storage

(MCM)

CCA

(ha) Nos.

Storage

(MCM)

CCA

(ha)

1 Shekhawati 47 30 5311 16 60 10917 63 90 16228

2 Ruparail 32 31 2229 22 71 32869 54 102 35097

3 Banganga 134 70 11854 62 342 55333 196 412 67187

4 Gambhir 75 46 8997 23 185 31591 98 232 40587

5 Parwati 9 7 794 8 151 30669 17 157 31463

6 Sabi 55 37 5252 12 71 14570 67 108 19821

7 Banas 1092 606 102367 222 3033 640289 1314 3640 742656

8 Chambal 135 112 18481 115 2795 1034932 250 2907 1053414

9 Mahi 190 133 19062 54 2594 222630 244 2727 241692

10 Sabarmati 39 101 4862 15 99 9117 54 200 13979

11 Luni 785 240 47947 119 897 162253 904 1137 210200

12 West Banas 4 2 456 15 77 14481 19 79 14937

13 Sukli 0 0 0 8 44 11194 8 44 11194

14 Other Nala 0 0 0 0 0 0 0 0 0

15 Out side 12 6 1085 2 3 818 14 9 1903

Total 2609 1421 228697 693 10421 2271662 3302 11842 2500359

1.2.2 Ramgarh dam

Ramgarh dam was being used as the major source of water supply in Jaipur city, but due

to some negligence and lack of proper management and maintenance, at present, the dam

is dry. This problem needs intensive study and research into the area. Keeping this view

in mind, the Ramgarh dam has been chosen as the study area.

It is an artificial dam created by constructing a dam across Banganga River. There

are hills on both side of the dam and have good storage space. The dam is situated at 25


9

Km. in the northeast direction from Jaipur city. The River Banganga rises from the

northeast part of the catchment area as a head-stream and flows down through southern

and then eastern sinuous path towards its destination to meet the dam near Ramgarh. The

Madhobini River accounts as another contributory to the Ramgarh dam, whereas the

Gumti ka Nala and its tributaries are lying towards its south. Salient features of the

Ramgarh dam are as shown in Table 1.3.

Table 1.3 Salient features of Ramgarh dam

S.N. Particular Detail

1 Catchment area (km2) 832.00

2 Average monsoon precipitation (mm) 559.00

3 Gross storage capacity (MCM) 75.00

4 Live storage capacity (MCM) 74.30

5 Length of dam (m) 1143 m

6 Height of dam (m) 23.16 m

7 Maximum flood discharge (m3/sec) 962.00

8 Culturable Command area (ha) 12125.00

1.2.3 Bisalpur Dam

Bisalpur dam is constructed in Banas basin. The catchment area of Banas basin is

bounded by the Luni basin in the west, the Shekhawati, Banganga and Gambhir basins in

the north, the Chambal basin in the east, and the Mahi and Sabarmati basins in the south.

The basin extends over parts of Jaipur, Dausa, Ajmer, Tonk, Bundi, Sawai Madhopur,

Udaipur, Rajsamand, Pali, Bhilwara and Chittorgarh districts. Geographically, the

western part of the basin is marked by hilly terrain belonging to the Arawali chain. East

of the hills lies an alluvial plain with a gentle eastward slope. The main tributaries of

Banas are Berach and Menali on the right, and Kothari, Khari, Dai, Dheel, Sohadara,

Morel and kalisil on the left. The Berach, Kothari, Khari are the rivers which flow

through districts Bhilwara, Chittorgarh, and Ajmer. Salient features are shown in Table

1.4.


10

Table 1.4 Salient features of Bisalpur dam

S.N. Particular Detail

1 Catchment area (km2) 27726.00

2 Average monsoon precipitation (mm) 588.00

3 Gross storage capacity (MCM) 1095.84

4 Live storage capacity (MCM) 1040.95

5 Length of dam (m) 574 m

6 Height of dam (m) 39.5 m

7 Maximum flood discharge (m3/sec) 33800.00

8 Culturable Command area (ha) 81800.00

The water stored in the dam is used for drinking water supply in Ajmer & Jaipur

districts (Table 1.5). The stored water is also used for irrigation of 81800 ha of

agricultural land in 256 villages of Tonk district for which 226.63 MCM water has been

allocated.

Table 1.5 Drinking water requirements from Bisalpur dam (MCM)

District 1991 2001 2011 2021

Ajmer 50.98 73.63 104.79 144.44

Jaipur 62.31 135.94 215.24 314.36

Total 113.28 209.57 320.02 458.79

Summary detail of the study area comprising Rajasthan state, Bisalpur dam, and

Ramgarh dam are shown in Table 1.6.

Table 1.6 Summary detail of study area

Particular Rajasthan state Bisalpur dam Ramgarh dam

Catchment area (Km2) 343,000 27,726 832

Latitude 23003‟00‟‟ to

30012‟00” N

24015” to 26015‟ N

(Dam: 25055‟20‟‟ N)

26055‟40” to 27026‟22” N

(Dam: 27˚02‟43” N)

Longitude 69030‟00” to

78017‟00” E

73025‟ to 75030‟E

(Dam: 75027‟30‟‟ E)

75040‟05” to 76016‟37” E

(Dam: 76˚03‟38” E)

River basin 15 River basins Banas basin Banganga basin

1.3 Need of the Study

The dams in the Rajasthan state are receiving less water than their theoretical yield/

designed capacity. The probable reasons are to be explored that have changed the


11

hydrological characteristics of water inflow to the dams. The regular phenomenon of

decreased water inflow to the dams has resulted in disruption of water resources planning

of the state. Non-receipt of computed water results in failure of water management plan

of the state. It leads to formation and execution of contingency plan on war footing every

year. No scientific research has been undertaken to investigate these issues and to develop

strategies, particularly in Rajasthan.

Due to less water inflow to the dams and an increase in the number of dark zone

blocks, the state is facing the severe water stress problem. With the increasing population

in the future, it will become more severe with the passage of time. Earlier Ramgarh dam,

which is now completely dry, had been used as drinking water source of Jaipur city. Since

the year 2000, the water level in the Ramgarh dam remains around zero level (Fig. 1c).

Bisalpur dam is being used for the same purpose, but Fig. 1(d) is showing the alarming

situation of this dam also. This has put water administrators, stakeholders, and decision

makers in an extremely problematic situation because of its severity and length. Ramgarh

and Bisalpur dams are related to the drinking water supply of capital city Jaipur;

therefore, special attention to Ramgarh and Bisalpur dams is needed. It is necessary to

manage decreasing water resources & increasing demand of water by maintaining the

ecological sustainability (Saito et al. 2012; Andrew et al. 2011; Takayanagi et al. 2011).

The author could not find any previous papers that studied scientifically the temporal

variation of inflow, causes, effects, and management specifically for Rajasthan state.

1.4 Objectives of the Study

Presently, the Water Resources Department (WRD) uses Strange‟s Table for

computations of theoretical yield and dependability analysis to assess the performance of

existing dams. Temporal variation of inflow has not been tested and scientifically

analyzed especially in Rajasthan. Moreover, the Ramgarh dam, a past source of the

drinking water supply to Jaipur city, has completely dried up. Bisalpur dam, the present

source of the drinking water supply is also facing the phenomenon of temporal variation

of inflow. Groundwater sources have been extracted and gone to the level of the dark

zone. Future of the city‟s drinking water supply is uncertain.


12

This thesis is aimed to test the hypotheses, whether there is an appreciable change

in water inflow to the dams of Rajasthan state with special reference to Ramgarh and

Bisalpur dams, determination of critical year which divides two consecutive non-

overlapping epochs- pre-disturbance and post-disturbance, to test the applicability of

prevailing method of inflow determination, and factors responsible for temporal variation

of inflow.

Looking to the gap in the existing knowledge and a burning issue to be

investigated, this research study has been carried out. The main objectives of this study

are (i) to test the temporal variation of inflow to the dams (ii) to analyze and estimate

changes in the inflow to the dams and various parameters affecting it (iii) to check the

applicability of the prevailing method of computation of theoretical inflow (iv) to find out

the critical year to bifurcate pre and post-disturbance period and to find out the principal

factor responsible for temporal variation of inflow.

By getting the critical year and principal components responsible for temporal

variation of inflow to the dams, results of this thesis will be useful to arrive at a

preventive and curative measure for less water inflow to the dams and for maintaining the

ecological sustainability.

1.5 Organization of Thesis

The thesis is documented in seven chapters. General introduction, study area, the need of

the study, and objectives of the present study are given in chapter 1.

Chapter 2 presents a comprehensive review of the literature, contains the various

research work undertaken in India and other parts of the world.

Chapter 3 consists material and methods, includes the data collection part and method

adopted for analysis.

Chapter 4 deals with the analysis, results, and discussion for the dams of Rajasthan state.

This chapter covers the data analysis adopting the methods discussed in chapter 3 along

with their results and discussion.


13

Chapter 5 deals with the analysis, results, and discussion for Ramgarh dam. This chapter

covers the data analysis adopting the methods discussed in chapter 3 along with their

results and discussion.

Chapter 6 deals with the analysis, results, and discussion for Bisalpur dam. This chapter

covers the data analysis adopting the methods discussed in chapter 3 along with their

results and discussion.

Chapter 7 presents the conclusions, highlights the significant conclusions derived by the

results and discussions.

These are followed by a list of references which are mentioned in this thesis and

appendices of some important tabulated data.

Study of Temporal Variation of Inflow to the Dams of Rajasthan State with Special Reference to Ramgarh and Bisalpur Dams.

14

2 LITERATURE REVIEW

2.1 Introduction

2.2 Data Period and Data Collection

2.3 Runoff and Inflow Variability to the Dams

2.4 Rainfall Variability and Trends

2.5 Climatic Factors and Climate Changes

2.6 Human Interventions and LULC changes

2.7 Ground Water Recharge/Depletion

2.8 Population Growth and Economic Expansion

2.9 Critical Year and Sensitive Analysis

2.10 Environmental Flow and Ecological Sustainability

2.11 Water Resources Planning and Management

2.11.1 Conjunctive Use of Surface and Subsurface

Water

2.11.2 River Basin Management

2.11.3 Small WHS and Large Dams

2.11.4 Micro Watershed Planning

2.11.5 IWRM and INRM

2.11.6 Transboundary Water Transfer and Water

Sharing

2.11.7 Water Pricing, Water Market, Water Trading,

and Water Privatization

2.11.8 Rain Water Harvesting


15

2.11.9 Reuse and Recycle of Waste Water

2.11.10 Water Balance

2.11.11 Water Use Efficiency

2.12 Summary


16

CHAPTER-2

LITERATURE REVIEW

2.1 Introduction

Water is central to the survival of life itself, and without it plant and animal life would be

impossible. Water is a central component of Earth‟s system, providing important controls

on the world‟s weather and climate. Water is also central to our economic well-being,

supporting rain-fed and irrigated agriculture, forestry, navigation, waste processing, and

hydroelectricity. Recreation and tourism are other primary uses supported by water

(Draper 2007). United Nation estimates as cited by Central Water Commission (CWC

2000), the total amount of water on earth is about 1400 million cubic kilometers (MCK)

which is enough to cover the earth with a layer of 3 km depth. Fresh water constitutes

only 2.7 percent of this enormous quantity, i.e. 37.5 MCK. About 75.2 percent of this

available fresh water, i.e. 28.43 MCK lies frozen in Polar Regions, and another 22.6

percent, i.e. 8.54 MCK is present as deep groundwater. The rest, 2.2 percent of available

freshwater or 0.06 percent of total available water, i.e. 0.83 MCK is in lakes, rivers, the

atmosphere, moisture, soil and vegetation of which small proportion i.e. 0.0487 MCK

(48700 BCM) is effectively available for consumption and other uses.

Average annual precipitation in India is 1182 mm which estimates about 4000

BCM precipitation over the country. The geographical area of the country is 3,287,300

km2. Average annual natural runoff in the rivers is 1869 BCM. Due to various constraints

of topography, uneven distribution of resources over space and time, it has been estimated

that only 1122 BCM can put to beneficial use. India is a second highest populated country

in the world, i.e. about 16.5 percent of world population (1.21 billion out of 7.35 billion

of the world population) resides in the country while utilizable water availability is only

2.3 percent.

Average annual precipitation of Rajasthan State is only 575 mm against the

national average of 1182 mm, i.e. less than half of the national average. As reported by

Majaya and Srinivasa (2014), Kerala receives an average rainfall of 3600 mm with

summer rains constitute about 10%. Spatial distribution of this rainfall is highly uneven

ranging 190 mm in Jaisalmer district (Western Rajasthan) to more than 1000 mm in


17

Jhalawar district (Southern Rajasthan). The geographical area of the state is 343,000 km2,

which is 10.4% of the country‟s geographical area. The population of the state is 68.6

million, which is 5.67% of the country‟s population. Livestock in the state is 60 million,

which is 20.5% of the country‟s livestock (292 million). After having this large

proportion of geographical area, population, and livestock the state is having only 16.05

BCM i.e. 1.43% of country‟s utilizable surface water resource. The crisis about water

resources development and management thus arises in Rajasthan firstly because of the

disproportionate availability of utilizable water and secondly it is characterized by its

highly uneven spatial distribution. Accordingly, the importance of water has been

recognized in the state, and greater emphasis is being laid on its economic use and better

management.

Changes in water level of the dams and reservoirs have been observed in different

parts of the world. Although the water in the dams, lakes and reservoirs represents a

relatively small percentage of total available water on earth, dams are used as a reliable

source of drinking water supply. Water availability in the dams is also an important

source of agricultural water need, power generation, and recreation. Changes in the water

levels are because of temporal variation of inflow to the existing dams. These changes

mainly reflect changes in rainfall, evapotranspiration (ET), infiltration, runoff and human

activities over the catchment area. Tiercelin et al. (1988) as cited by Yildirim et al. (2011)

observed that these fluctuations constitute a sensitive indicator of past and present climate

and human activity changes at a local and regional scale.

Many investigations in different parts of the world (De et al. 2001, Yan et al.

2002; Penny and Kealhofer 2005; Legesse and Ayenew 2006; Kiage et al. 2007; and

Kravtsova et al. 2009 as cited by Yildirim et al. 2011) have noted shrinkage of lakes due

to anthropogenic activities such as land cover and land use changes, deforestation, rising

water demands for agriculture and livestock, urbanization, water abstractions upstream of

the lakes, dam construction and irrigation. However, further drying of dry areas in Asia is

very likely (Kravtsova et al. 2009). Han et al. (2012) revealed the fact that today, 1.4

billion people live in river basins where water utilization exceeds sustainability

thresholds; of this 4 % live in China. More than 70% of the waters in these basins (Hai,


18

Huai, and Yellow river basins of China) are polluted, runoff decreased 60%, and

groundwater withdrawal is 150% of the sustainability threshold. In some cases, changes

in climatic fluctuations, especially precipitation, are believed to be the major causes of

lake shrinkages (Birkett 1995; 2000; Moln‟ar et al. 2002; Mercier et al. 2002; Mendoza et

al. 2006; Medina et al. 2008 as cited by Yildirim et al. 2011). The hydrological-

environmental changes that took place in the recent decades in the Indus Delta can serve

as an example of the catastrophic consequences that can be expected to take place in the

deltas of world rivers with anthropogenic drop in water and sediment runoff, especially

under the conditions of extremely dry climate and progressive global warming

accompanied by sea level rise and increasing sea waves. In recent years, the population

suffered from freshwater deficiency. Part of the population had to leave the sites of their

former residence (Kravtsova et al. 2009). Analysis of temporal variations of inflow to the

dams is an important task that has applications in different fields of water resources

planning and management.

Various recent studies, literature, newspapers (Rajasthan Patrika, Dainik Bhaskar),

water experts and even Rajasthan High Court are continuously reporting about the great

concern of less water inflow in the dams, lakes and reservoirs in the state. All land as

drainage channels like streams, rivers, tributaries, etc. as of 15.08.1947 should be

declared as Government land. Any conversions made after 15.08.1947 should be declared

illegal. The relevant rules and act must be amended accordingly (Decision Rajasthan

High Court 2004). The major concern was put forward by the expert committee

constituted by the Court towards restricting the encroachment in the drainage channels

and catchment area of the existing dams in the state. A scientific study is essential to be

conducted about the problem cited above.

2.2 Data Period and Data Collection

Accurate and reliable data and information of source water supplies are key requirements

in effective water planning and management (Draper and Kundell 2007). Fulp and Frevert

(1998) and Davidson et al. (2002) as cited by Frevert et al. (2006) explained the data-

centered approach allows for a variety of data sets to be used. The required datasets are

rarely available, even in the highly instrumented watersheds (Pundik 2014). The most

commonly used is the Hydrologic Data Base (HDB), HDB is a relational database


19

designed for the storage of hydrologic time series data, attribute data, statistical

information, and other data related to water resources management activities. HDB is

capable of maintaining over 100 types of data, including, for example, stream flow,

reservoir content and releases, water demands, temperature, snow water equivalent,

precipitation, evaporation, power generation, and total dissolved solids. In so doing, it

provides a reliable and consistent view of the past, present, and the future state of the

river system. Only a few quantitative analyses have been performed due to lack of

information (data), have been mentioned in the respective research article (Kim et al.

2012). Information technologies provide significant support in the management of lakes

and reservoirs. The aim of the data analysis is to determine still unknown relations

(dependence) between entity attributes, their mutual characteristics, and prediction of

future behaviour. Data analysis enables making conclusions and conducting appropriate

measures within managing lakes and reservoirs according to proposed objectives.

Information System (IS) provides all forms of management and sustainable exploitation

of water resources in whole and enables the transfer of information and knowledge as

well as the creation of a network of researchers. It is a necessary step towards improving

the current situation in the management of lakes and reservoirs (Stefanovic et al. 2012).

The question of reliability of past hydrologic records may be a significant

problem. Even what appear to be long-duration records (say 50 or 100 years) may not be

representative of the cycles of hydrologic variation. Man-made changes in flow

conditions (from storage, diversions, and changes to impervious surfaces) may skew the

reliability of historical data (Guo 2006 as cited by Dourte et al. 2012, Draper 2007a). A

case study of Chicago urban drainage systems showed that drainage system using updated

IDF relationships, using shorter records of more recent data, performed significantly

better than those developed from older rainfall records (Dourte et al. 2012). Karamouz et

al. (2010) used 23 years length of historical data as a planning horizon of the model. The

task of data collection is difficult, time-consuming and can be impossible for a huge

region when using traditional ground survey techniques. Remotely sensed data acquired

by operational satellites are more and more widely used for the identification, monitoring,

and delineation of lake mapping at regional or global scales (Yildirim et al. 2011). The

government conducts regular ground survey for day to day activity and events happening


20

within the state. Directorate of Economics and Statistics (DES) publishes various forms

of data and information every year for the state and every district within the state. The

government of India through its various departments, e.g. Indian Meteorological

Department (IMD), Planning Department, Agriculture Department, and Forest

Department, etc. also publishes various forms of information and data. Frevert et al.

(2006) suggest that it takes the full 3 to 4 year time frame to conduct the new research and

development, demonstrate the applicability, and deploy the system. Hassanzadeh et al.

(2012) used 40 years data set for system dynamic modeling. Section 4.2 of IS 5477 (Part-

3): 2002 recommends stream flow data at the site of interest or for upstream, downstream

or nearby station for 25 to 40 year period. In the present study, around 25-35 years of data

has been used for input-output analysis. Some of the data available are irregular, which

has been filled up by interpolation technique. State Water Resources Department

measures daily rainfall; therefore, hourly rainfall data is not available.

2.3 Runoff and Inflow Variability to the Dams

Surface runoff and inflow assessment to the dams is important for study of hydrological

behavior of any reservoir. Runoff variability and changes in water inflow to the dams

have been observed by many researchers all over the world. At many places catchments

are found ungauged. At many places hydrological models have been developed for runoff

computations. Performance statistics have also been applied at many places.

River runoff in Africa has reportedly declined by about one-third in the last ten

years (Draper 2007a). In a recent study by Selyutin et al. (2007) explained that

hypertrophic withdrawal and consumptive use of surface waters, results in a decline in the

volume and qualitative characteristics of freshwater runoff, and wide occurrence of soil

erosion. A commonly used ranking describes three levels of severity (i.e. Can be named

pre-alarm, alarm, and emergency). Ground water resources play a vital role in meeting

water demands, not only as regards quality and quantity, but also in space and time, and

are of vital importance for alleviating the effects of droughts. Ground water reserves have

been and continue to be the largest buffer in water scarcity situations, but a large range of

negative effects has been documented for its overuse (Iglesias et al. 2007, Sang et al.

2010). As reported by Shah and Kumar (2008), many dams in India do not get sufficient

storage, due to inadequate inflows from their catchments. Reliable data on Indus sediment


21

runoff are practically impossible to obtain (Kravtsova et al. 2009). Annual discharge

records on the Akarçay River and its tributaries decreased over the basin during the same

period. Irrigation systems, three dams and seven pounds built in recent decades for

agricultural irrigation and domestic use, made the major impact on lowering the lake

levels because they derive water from the river for human use upstream of the lake

catchments. Human activities related to water resources management under dry farming

conditions sometimes lead to a considerable decrease in river water runoff; therefore, dry

farming is included into the group of water consumers. Intensive agricultural practices,

the organization of field protective forest strips, special technical methods meant to retain

moisture in the soil (deep fall ploughing, high stubble left after harvesting, snow

detention, and contour ploughing) result in an increase in soil moisture capacity, and crop

yields and, thus in a decrease in surface flow discharge into water receivers (Denim

2010). According to an assessment of N.I. Koronkevich as cited in Denim A.P. (2010),

under the impact of agrotechnical methods and agricultural afforestation “the river water

runoff decrease in the entire Russian plain was less than five km3/year, including the

Volga river water runoff decrease equaling two km3/year.

In China, with the development of the economy, increasing population and

improved standard of living, the industrial and living water use is increasing, which is

affecting the runoff (Sang et al. 2010). Russia is also observing inflow changes at global

warming in the 21st century (Lemeshko 2011). Sun et al. (2012) reported that the water

level of Three Gorges Dam (TGD) decreased 2.03 m in 2006 and 2.11 m in 2009 at the

outlet of the lake, with an extreme decrease up to 3.30 m and 3.02 m, respectively. For an

ungauged basin to be similar to a gauged basin, it is not sufficient to have basin

characteristics only. Soil type, land use, as well as climatic conditions are equally

essential to be similar (Piman and Babel 2012). Zhao and Xu (2013) state that biocrusts

play an important role in soil erosion control from water erosion in semiarid regions,

although there was a potential increase in runoff yield. The percentage coverage of

biocrusts may be 70% in such regions, which play several critical roles in arid and

semiarid ecosystems, such as increasing soil fertility, inhibiting or promoting surface

water infiltration, influencing soil moisture evaporation, and improving soil properties,

e.g. fertility, texture, and so on. There is less certainty on how the presence of biocrust


22

actually influences the water infiltration and runoff relationships. A major part of the state

and its river are ungauged and therefore very less, and irregular data are available for

river discharges over different time and space. According to Parmar et al. (2014), runoff

information is needed for a multitude of purposes such as water resources management,

assessment of hydropower potential, the design of spillways, culverts, dams, and levees,

for reservoir management, water quality issues, etc. Most of the river basins are ungauged

and predicting runoff in these catchments need to adopt an alternative approach. Surface

runoff has been computed with the help of inflow data of the existing dams. Predicting

hydrologic quantities (rainfall, runoff, sediment, nutrients, etc.) is a major challenge for

hydrologists and water resources managers since hydrological observations are either

absent, insufficient or of questionable quality and reliability (Chunale et al. 2014a).

Estimation of peak flow and the total volume of water becomes a difficult task for the

ungauged catchment (Nayak 2014). The relationship between rainfall and runoff is

uncertain. There are two types of relationships, i.e. linear and non-linear. The linear

relationship between rainfall and runoff is found for small catchments while the non-

linear relationship for large catchments. The non-linear relationship may be in power,

logarithmic or exponential form (Parmar et al. 2014).

Runoff computation is one of the most requirements in hydrology. The rational

method AICKQpeak is one of the simplest and well-known methods of

hydrology. It computes peak discharge for a drainage area based on rainfall intensity and

hydrograph shape. Williams et al. (2008) evaluated hydrological response from

watersheds influenced by various agricultural land management practices using a daily

time step hydrology model based on the temple water yield model with

evapotranspiration (ET) and percolation components added. Dune (1978) as cited by Alfa

et al. (2011) proposed saturated excess overland flow (SOF). If rainfall intensity exceeds

infiltration capacity of the soil, rain will accumulate on the surface depending upon the

surface roughness, the gradient of the land surface and the available depression storage,

Hortonian Overland Flow (HOF) may occur, which is also termed as infiltration excess

overland flow (IOF). Different catchments respond differently to rainfall in terms of the

hydrological processes on the land surface. The nature of the overlying soil and the type


23

of bedrock determine the amount of runoff that will be generated and the path it will

follow to reach a stream channel (Alfa et al. 2011). Wang et al. (2011) describe the VIC

model (Variable Infiltration Capacity) that is used to simulate the physical exchange

processes of water and energy in the soil, vegetation and atmosphere in a surface

vegetation atmospheric transfer scheme. Three types of evaporation are considered:

evaporation from the wet canopy, evapotranspiration from the dry canopy, and

evaporation from bare soil. The total runoff estimates consist of surface flow and base

flow. Soil column is divided into three layers. Surface flow, including infiltration excess

flow and saturation excess flow, is generated in the top two layers only (Wang et al.

2011). Chang et al. (2011) used System Dynamics model, which is a computer-aided

approach to evaluating the interrelationships of components and activities within complex

systems Hydrological model component is applied in SWAT using the Thiessen

weighting technique (Kim et al. 2012). The SCS-CN is also an empirical, event-based

rainfall-runoff model. The dimensionless curve number (CN) takes into account, in a

lumped way, the effect of land use/cover, soil type, and hydrologic conditions on surface

runoff and relates direct surface runoff with rainfall (Migliaccio and Srivastava 2007,

Chunale 2014a, Nayak 2014). However, using software like Watershed Modeling System

(WMS) requires both knowledge and experience on watersheds and physical hydrology,

as well as computer skills (Erturk et al. 2014). Mortin et al. (2006) as cited by

Laxminarayana et al. (2014) found that complex interactions exist between the

spatiotemporal distributions of rainfall systems and hydrological responses in a

watershed. Artificial Neural Network (ANN) and other soft computing techniques are

inherently suited to the problems that are mathematically difficult to describe. Due to

acceptable performance in R-R modeling, ANN remains a topic of continuing interest.

Radial Basic Function (RBF) ANN has mostly used a type of ANN in hydrological

modeling. Levenberg-Marquardt (L-M) algorithm is much more robust and outperformed

other algorithms. Most commonly used transfer functions are sigmoidal type in hidden

layers and linear type in output layer due to its advantage in extrapolation beyond the

range of the training data. Early stopping approach (split sampling method) & so called

batch training approach, i.e. the whole training data set is presented once, after which the

weights and biases are updated according to the average error, was used (Pundik et al.


24

2014). Hydrological modeling for inflow using Water Evaluation and Planning System

(WEAP), ET Toolbox, River ware, Stochastic Analysis Modeling and Simulation

(SAMS), System Dynamic and Impact Analysis, Sensitivity Analysis, Storm Water

Management Model (SWMM), Multivariate Statistical Methods (MSM), Soil and Water

Assessment Tool (SWAT/modified SWAT), General Circulation Model (GCM), SCS-CN

methods, TOPMODEL (a type of saturation excess runoff prediction model),

MODFLOW, HEC-HMS, Implicit Stochastic Optimization(ISO), Water Resources Yield

Model (WRYM) etc. techniques can be undertaken. These techniques have been used by

many researchers (Frevert et al. 2006; Migliaccio and Srivastava 2007;, Jain et al. 2008;

Wang et al. 2008; Yates et al. 2009; Karamouz et al. 2010; Mantel et al. 2010; Sang et al.

2010; Carrasco et al. 2011; Chang et al. 2011; Hen et al. 2011; Wang et al. 2011; Bobba

A. G. 2012; Hassenzadeh et al. 2012; Kim et al. 2012; Lany et al. 2012; Piman and Babel

2012; Chunale et al. 2014a; Chunale 2014b; Nayak 2014; Sadeghi et al. 2014). For

limited availability of regional data, conceptual or empirical methods like Strange‟s Table

were also suggested (Water Resources Department 2002; Reddy n.d.; Sharma and Sharma

n.d.; Punmia n.d.; Satyanarayana murty 2006; State Water Resources Planning

Department 2011; Subramanya 2013). Due to limited availability of regional data, the

Strange‟s table method of computation of theoretical inflow has been adopted in this

thesis and applicability of this method has also been tested using various statistical

parameters.

T-test, Chi Square test and some other statistical test are applied to test the

hypothesis whether the existing dams are getting the designed water or not. Six different

statistical parameters were employed in judging the performance of the simulation model.

Several researchers have used many of the following statistical parameters for judging the

performance of simulation model: Sum of Square Error (SSE), Relative Error (RE),

Percent Error, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean

Absolute Percentage Error (MAPE), Nash-Sutcliff Efficiency (NSE), Coefficient of

Correlation (R), root mean square error of the observation‟s standard deviation ratio

(RSR), Normalized Mean Bias Error (NMBE), Percentage of Peak flow Error (PPE), and

Percentage of runoff Volume Error (PVE) (Hayashi et al. 2008; Wang et al. 2011;


25

Hassanzadeh et al. 2012; Piman and Babel 2012; Kumar et al. 2014; Chunale 2014a;

Chandwani et al. 2015).

A positive NMBE indicates over-prediction, and a negative NMBE indicates

under-prediction of the model (Srinivasulu and Jain 2006). The coefficient of efficiency

(E) or Nash-Sutcliffe efficiency (Nash and Sutcliffe 1970) is a ratio of residual error

variance of measured variance in observed data. A value close to unity indicates the

accuracy of the model. RSR statistics was formulated by Moriasi et al. (2007). RSR

incorporates the benefits of error index statistics and includes a scaling/normalization

factor so that the resulting statistic and reported values can apply to various constituents

(Chen et al. 2012). The optimal value of RSR is zero. Hence, a lower value of RSR

indicates good prediction. RMSE statistic compares the observed values to the predicted

values and computes the square root of the average residual error. A lower value of

RMSE indicates good prediction performance of the model. But RMSE gives more

weight to large errors (Kisi et al. 2013). MAPE is a dimensionless statistic that provides

an effective way of comparing the residual error for each data point on the observed or

target value. Smaller values of MAPE indicate better performance of the model and vice

versa. Pearson‟s correlation coefficient (R) and coefficient of determination (R2) measure

the strength of association between the two variables. R and R2 statistics are dependent on

the linear relationships between the observed, and predicted values may sometimes give

biased results when this relationship is not linear or when the values contain many

outliers. In perfect association between the observed and predicted values, the value of R2

is unity. The t-test can be conducted to test the hypothesis (H0: r=0). NMBE measures the

ability of the model to predict a value which is situated away from the mean value. A

combined use of the performance metrics narrated above can provide an unbiased

estimate for prediction ability of any simulation model (Chandwani et al. 2015).

2.4 Rainfall Variability and Trends

Accurate and current rainfall characterization is an important tool for water-related

system design and management. To study the hydrology of any catchment area or any

basin, it is the first requirement to get an idea about the existing rain gauges in and around

the catchment. Rainfall data of the stations are collected from authentic sources for

further analysis and various related computations. General terminology used by Indian


26

Meteorological Department (IMD) in weather bulletins is mentioned in Table 2.1 & Table

2.2.

Table 2.1 Classification of intensity of rainfall

S.N. Rainfall Period Type of rain

1 00.1 mm to 02.4 mm In 24 hrs. Very Light Rain

2 02.5 mm to 07.5 mm In 24 hrs. Light Rain

3 07.6 mm to 34.9 mm In 24 hrs. Moderate Rain

4 35.0 mm to 64.9 mm In 24 hrs. Rather Heavy Rain

5 65.0 mm to 124.9 mm In 24 hrs. Heavy Rain

6 Exceeding 125 mm In 24 hrs. Very Heavy Rain

Table 2.2 Spatial distribution of weather phenomenon (percentage area covered)

S.N. Percentage Terminology used

1 01 to 25 Isolated

2 26 to 50 Scattered

3 51 to 75 Fairly Wide Spread

4 76 to 100 Wide Spread

Dourte et al. (2012) classifies high-intensity events if daily rainfall 50 mm/day

and low to moderate in case of 5-50 mm/day. Precipitation over the state is very scanty

and non-uniform over time and space. Annual rainfall varies from 190 mm in the west to

1000 mm in the south/east. As cited by Iglesias et al. (2007), climate change projections

for the Mediterranean region derived from global climate model driven by socio-

economic scenarios result in an increase of temperature (1.5 to 3.60C in the 2050s) and

precipitation decreases in most of the territory (about 10 to 20% decreases, depending on

the season in the 2050s). Climate change projections also indicate an increased likelihood

of droughts and variability of precipitation – in time, space, and intensity – that would

directly influence water resources availability.

Daily precipitation data of 48 years were analyzed by Erturk et al. (2014) to obtain

average annual rainfall, average monthly rainfall, and average daily rainfall for integrated

management of a watershed in Turkey. Daily precipitation data are available in Rajasthan


27

state also, and the same have been used in this study. However, some other researchers

like Dourte et al. (2012) developed IDF curves for return periods of 2, 5, 10, 15, 20, 50,

75, and 100 years for 1, 2, 4, 8, and 24-hour duration. Thiessen polygon technique can be

used to compute the weighted rainfall (Jain et al. 2008, Piman and Babel 2012); the same

has been used in this study. In the present study IDF curves could not be prepared

because of non-availability of hourly precipitation record in WRD. Piman and Babel

(2012) emphasizes that a number of rain gauges for rainfall estimates affects the

magnitude of rainfall estimates significantly and, subsequently has a considerable effect

on calibrated model parameters. The linear relationship is the most common method used

for detecting rainfall trends. Besides Man Kendall test can be used to assess the

significant trends in hydrological and climatological data sets (Laxminarayana et al.

2014).

2.5 Climatic Factors and Climate Changes

The intergovernmental panel on climate change (IPCC 2007) as cited by Draper and

Kundell (2007) published a report substantiating the argument that global warming is

occurring. The IPCC reported that while sustainable water yields may or may not be

reduced in long-term average, they will almost certainly be less reliable in the short term.

The climate change challenges existing water resource management practices by adding

uncertainty. Climate change will substantially reduce available water in many of the

water-scarce areas of the world, but will increase it in some other areas. Freshwater

availability in central, south, east and southeast Asia, particularly in large river basins are

projected to decrease due to climate change which, along with population growth and

increasing demand arising from higher standards of living, could adversely affect more

than a billion people by 2050‟s. Runoff and water availability is expected to decrease in

arid and semiarid Asia (Cosgrove 2005; Wurbs et al. 2005;, Draper and Kundell 2007;

Shiklomanov et al. 2011; Acharya et al. 2011; Keng et al. 2012). Climate change and

overuse of surface water resources, constructing four dams in u/s, less precipitation are

the main factors responsible for declining Urmia Lake‟s level in the recent years

(Hassanzadeh et al. 2011). Effect of climate change resulted in decreased annual rainfall

leading to drought. Water inflow into Bhakra and Pong dams remains low (BBMB

Report as cited by www.dnaindia.com). The impact of global warming is uncertain


28

(Draper 2007, Lemeshko 2011). A study done by Comair et al. (2012) showed that 88%

of all precipitation within the Jordan River Basin is lost to ET. This matches well with

estimates in the literature, which ranges 85 to 90%. The basin-wide ET average is 247

mm/year. Kar D. (2011) pointed the effect of global warming, climate change and various

other factors that availability of water in the rivers and lakes in India is diminishing at an

alarming rate and also mentioned that impact of global climate change is making the

prediction of rainfall pattern quite uncertain. Because of the dry climate, water resources

in this region serve as a factor that limits the socio-economic development (Selyutin et al.

2009). Both water availability by storage in small reservoirs and the yield of large

reservoirs are strongly sensitive to plausible climate change (Krol et al. 2011). Five daily

climatic parameters viz. max. temp., min. temp., precipitation, wind speed, relative

humidity, and solar radiation have been used in this thesis.

2.6 Human Interventions and LULC Changes

Rapidly increasing population and economic development in many basins often tightly

constrain the allocation of limited water supply (Rebecca and Teasley 2011). Population

growth, rising water consumption for agricultural and domestic purposes and building

dams has led to lake levels declining (Unal et al. 2010; Sang et al. 2010; Lemeshko 2011;

Shiklomanov 2011). Kim et al. (2012) shown that changes in streamflow responses

attributable to the farm dam development were not easily distinguishable from those

attributable to land use and a more mechanistic loss module that accounts for direct losses

caused by farm dams is required. A Study done by Petrov and Normatov (2010) reveals

that excessive development of irrigated farming in the region in 1960‟s-1990‟s, resulting

in a strict demand for practically complete regulation of river runoff, both seasonally and

over years, and its complete consumption for irrigated farming. It was this approach that

has resulted in drying of the Aral Sea with catastrophic environmental consequences. Due

to the effects of man-made structures, the recharge quantity for impermeable zones is zero

(Chang et al. 2011). The type of anthropogenic impact that are hazardous to the

reproduction of water resources include excessive water withdrawal (from both surface

and subsurface sources), mine workings, melioration systems, hydraulic structures, road

construction, pollution of water bodies by wastewater discharges, washout of pollutants

from agricultural lands and urban areas by floods or rains, airborne pollution, and the like.


29

The mechanism of such impacts is not completely understood (Danil‟yan 2005). The

response of river channels of TGD to human intervention has been long recognized and

well documented (Sun et al. 2012).

Population rise is giving impetus to agricultural development and agriculture is

the lion‟s share of overall water use, with substantial in-stream and environmental

requirements, while urban water use is only about 5% of the total annual requirement

(Yates et al. 2009). The consequences of large-scale hydraulic engineering activity (large

dams and reservoirs, numerous low head barrages on the Indus and its tributaries and an

increase in water withdrawal for dry land irrigation are a subject of intense studies in

recent decades (Kravtsova et al. 2009). According to an assessment of Hayashi et al.

(2008), approximately half of the world‟s population is living in urban areas. Land use

modification associated with urbanization, such as removal of vegetation, replacement of

previously areas with impervious surfaces and drainage channel modification invariably

results in changes to the characteristics of the surface runoff hydrology. In a basin,

reservoir density is considered high, when the capacity of reservoirs exceeds 40% of

average runoff, a situation plausibly nearing basin closure, as an indicator for

unsustainable human interferences in the river basin. Hayashi et al. (2008) classified the

land cover into seven types: forest, bush and shrubs, grassland, farmland, paddy field,

urban area, and wastelands such as bare land and rocky land. A key objective of water use

regulation in foreign documents is the achievement of a “good condition of water bodies”

by which is meant their general state when equilibrium is attained between variations and

anthropogenic impact on the one hand, and the ecological functions of water on the other

hand (Kravtsova et al. 2009; Khaustov and Redina 2010). Since the 1960s, water

depletion in the Western Inner Mongolia basin has increased very rapidly due to intensive

irrigation development in the middle stream area of the basin. As a result, the downstream

Erjina Oasis gradually receded and the destination inland lake disappeared. Xu and Li

(2010) as cited by Han et al. (2012) show that water demands in Chinese river basins are

increasing by 2-10% annually. At the same time, flow in the Hai, Huai, and Yellow

Rivers is decreasing due not only to water demands, but potentially due to climate

change, construction of dams and reservoirs, and land use change. As human activity

intensifies changes in land cover and disturbs the natural hydrological cycle, the


30

behaviour of the of the runoff time series will change (Wang et al. 2011). Human

interventions, anthropogenic changes, and LULC changes has been considered in this

study as a principal component responsible for temporal variation of inflow to the dams.

2.7 Groundwater Recharge/Depletion

In the study of Hayashi et al. (2008), it is explained that soil layer is vertically divided

into three storages (upper zone, lower zone, and active groundwater) in each hydrological

response unit (HRU). Runoff and evapotranspiration are calculated by the moisture

condition in each of the storage. Three runoff components- surface runoff, interflow, and

active groundwater runoff are considered in the hydrological processes. Each runoff

volume is obtained according to a nonlinear function of the storage volume in each zone.

When groundwater resources are not very much reliant and irrigation, major urban and

industrial uses in the basin are extremely reliant upon surface water, the groundwater

resources may not be considered in the study (Wang et al. 2008).

In an average year, a larger share of demand is met from surface supplies, where

in times of shortage, groundwater supplies make up a larger share, and there is greater

water shortage or unmet demand (Yates et al. 2009). Karamouz et al. (2010) used the

method of Thiessen area for computation of drawdown of groundwater level as shown

below:

AS

Vh

(2.1)

Where A= Thiessen area; ∆h= drawdown of groundwater level; ∆V= change in aquifer

volume; and S= storage coefficient.

Chang et al. (2011) used the following equations for the determination of recharge

quantity as shown below:

tAR 4 (For wetlands) (2.2)

Where A4 is the total area of wetland, t is a time interval of each time step, denotes

the constant infiltration rate of saturated soil (L/T) varies according to soil coverage type.

PAR 5 (For dry lands) (2.3)

Where A5 is the total area of dry land, P is the total rainfall quantity in the time interval,

denotes rainfall recharge coefficient varies according to soil type.


31

Ground water resources may be termed as subsurface hydrosphere resources. It

includes all waters below land surface and in the saturation zone that are in direct contact

with soil or grounds (Khaustov and Redina 2011). Due to this situation, infiltration and

further percolation are increasing, making less contribution of base flow to the surface

flow and thereby reducing the surface runoff. Due to excessively pumped withdrawal of

groundwater and corresponding negligible recharge by a natural process, the groundwater

is steadily dropping. Also, contamination of groundwater due to arsenic, various salts and

saline water intrusion in the coastal areas is a great hazard to public health (Kar D. 2011,

Dourte et al. 2012). The water planning and management policies of the Rio Grande

basin, one of the most stressed basins, not only due to the increase of population and

industry, but because of the natural water scarcity in the region, no longer respond to the

sustainable needs of economy, environment, health and quality of life for the people and

international commitments of this transboundary basin between Mexico and the United

States. In these circumstances, results (Solis et al. 2011) show that groundwater banking

can significantly improve water management in the basin, increasing system storage,

improving water supply for users in the basin, and enhancing compliance with the treaty

obligations. Thomas et al. (2001) as cited by Solis et al. (2011) pointed out that since the

1970s; groundwater banking studies have considered the economic and the hydraulic

feasibility of storing water in aquifers in wet periods and recovering it later in dry periods.

The development of groundwater banks requires the assessment of hydrogeology and

water quality, legal and financial issue, as well as proper water planning and

management. We should also think of cities that are green with vegetation that effectively

and substantially reduces the need for stormwater, sewers and instead promotes runoff

infiltration into the ground (Saito et al. 2012). With urbanization, the permeable soil

surface area through which recharge by infiltration can occur is reducing. An infiltration

trench alone or in combination with another stormwater management practice is a key

element in present-day sustainable urban drainage system (Chahar et al. 2012). Intensive

use of groundwater resources for agricultural production is proving to be catastrophic in

many arid and semiarid regions of the world, including some developed countries like

Spain, Mexico, Australia, and parts of the US, and developing countries like India, China,

and Pakistan, etc. (Shah and Kumar 2008). Total (761BCM) and agricultural (688 BCM)


32

water withdrawals in India are highest in the world. More than half of the irrigation

requirements of India are met from groundwater, and the number of mechanized bore

wells in India increased from 1 million in 1960 to more than 20 million in 2000. A recent

groundwater depletion study in the northwestern Indian states of Haryana, Punjab, and

Rajasthan, is illustrative of common regional groundwater depletion problem in India. For

the agricultural regions of semi-arid India, groundwater recharge is of major concern:

groundwater depletion is common as a result of large withdrawals. Given the scarcity and

seasonality of surface water resources in the region, increased rainfall variability

increases the reliance on groundwater resources for irrigation (Dourte et al. 2012).

With the increasing demand for water due to population growth and resulting

increase in agricultural and economic activities, groundwater extraction is increasing at a

very fast pace, resulting in rapidly lowering of water table year after year (Parmar et al.

2014, Nayak 2014). Decline of ground water level below ground level is assessed in this

study as one of the principal component responsible for less water inflow to Ramgarh and

Bisalpur dam.

2.8 Population Growth and Economic Expansion

India is having one-sixth of world mankind and showing a steady growth in the economy

and technology. By 2050, India‟s population will grow to approximately 2.0 billion. The

population of Rajasthan state is 70 million and will grow to 125 million by 2050. Urban

population has increased from 10.84% to 31.16% and 15.6% to 24.89% from 1901 to

2011 in India and Rajasthan respectively. An important recommendation of Ministry of

Water Resources, China, as cited by Lui et al. (2005), has been made for considering the

carrying capacity “determining the size of grasslands, according to water availability and

determining the stockbreeding size according to the stock-carrying capacity of grasslands.

Considering the rate of increasing population and the improving economy, it is

necessary to have long-term planning to balance the supply and the demand

distribution. As the water demand keeps increasing, the available water supply in

some years will be unable to meet the high demand which is also described by Liu et

al. (2005) and by Danil’yan (2005) as shown in fig 2.1.


33

Fig. 2.1 Scheme of formation and aggravation of freshwater deficiency (Danil‟yan 2005)

Some analysts have concluded that globally, water availability has declined

twofold during the past 25 years because of population growth and drier trends in the

climate (Draper 2007a). A study done by Kar D. (2011) shows the country is heading

towards rapid urbanization inhabitation pattern whereby 50% of the Indian population

will perhaps be living in cities and urban conglomerates in the near future. The problem

of water resources planning and implementation is somewhat unique in nature because of

its very high population density and its well established democratic set-up. India and

China together constitute about one-third of mankind. They are frequently compared as

both are having the mega population, little realizing that China has three times the area

with a marginally higher population hence, a much thinner population density. China has

strict land acquisition act for national projects while such laws are yet to be enacted in

India. Many researchers (Liu et al. 2005; Iglesias et al. 2007; Wang et al. 2008;

Karamouz et al. 2010; Wang et al. 2011; Shiklomanov et al. 2011; Han et al. 2012; Lany

et al. 2012; Uniyal et al. n.d.) have emphasized the negative impact of population growth

and economic expansion of water resources management. This study also highlights the

effect of increase in population density on inflow to the Ramgarh and Bisalpur dams.

Population growth, desire

to improve living

standard

Orientation to extensive

factors

Extension of water use

Growing anthropogenic

load onto water bodies

Depletion of water,

water quality deterioration

Water deficiency


34

2.9 Critical Year and Sensitivity Analysis

Time series analysis and sequential cluster analysis are applied to determine the critical

year, which divides two consecutive non-overlapping epochs; pre-disturbance and post-

disturbance (Wang et al. 2001).

Karamouz et al. (2010) conducted a sensitivity analysis to evaluate the effects of

agricultural price and the crop per drop (CPD) coefficient on the net benefit. International

norms for CPD coefficient is above 1 kg/m3 of water use. Several researchers (Wang et

al. 2008, Karamouz et al. 2010) conducted the sensitivity analysis to see the impacts of

various input parameters in the output parameter. In this study critical year has been

identified using the time series analysis and sequential cluster analysis. Cosine amplitude

method of sensitivity analysis has been used in this study to determine the principal

components responsible for temporal variation of inflow to the dams of the Rajasthan

state and specifically to Ramgarh and Bisalpur dams.

2.10 Environmental Flow and Ecological Sustainability

Reduced water inflows to the dams not only affect the human society but also adversely

affect the ecology also. It becomes very difficult to release the minimum environmental

flow (MEF) for ecological sustainability, which allows the fish to reach the spawning

sites in the periods of mass spawning run. There are many evidences when MEF was not

released because of the reduced inflow to the dams and requirement of irrigated farming

resulting in catastrophic environmental consequences with negative implications towards

current and future sustainability (Liu et al. 2005; Iglesias et al. 2007; Jain et al. 2008;

Yates et al. 2009; Petrov and Normatov 2012; Sun et al. 2012; Kumar 2014). Riverine

ecosystems and riparian rights should be appropriately considered during water resources

planning and management (Liu et al. 2005; State Water Policy 2010). Ecological runoff

(unregulated river) and ecological release (regulated river) along with admissible water

withdrawal (AWW, which varies from 5 to 20% of the annual mean value of the natural

river runoff) is computed for ecological equilibrium. Twice of the AWW is termed as

crisis state and more than twice is considered as disaster state (Danil‟Yan et al. 2006).

Lee et al. (2008) conducted a study and concluded that hog farming and municipal waste

water are the major causes of the deterioration of water quality. GIS and GPS were

applied to aid in non-point source (NPS) pollutants investigation and analysis. Both GIS


35

and GPS systems are important means for catchment land use identification. Use of water

banks, water exchange, and other best management practices (BMP) and enforcement of

relevant regulations are essential to manage effectively the river and control the pollution

(Frevert et al. 2006; Lee et al. 2008). The concentration of population and industrial

production in the region, unpractical industrial specialization, the anti-environmental

structure of the economy along with the degradation of resource-saving and high

technology plants have resulted in a heavy environmental pollution (Demin 2005).

Neumann et al. (2002) as cited by Frevert et al. (2006) specified the key issues in the

Truckee River basin and the Columbia Basin Project are Native American water rights,

endangered species issues, competing uses of water including agricultural, municipal,

recreational, and hydropower, and water quality. Applications of technology and

development work in these basins have been on a need-driven basis. Hernandez et al.

(2011) developed a hydrologic-economic-institutional model of environmental flow

strategies for environmental flow allocations in managing river systems to protect or

restore river ecosystems. Several methodologies have been applied worldwide to estimate

flows for environmental requirements. Specification of flow can range from a simple,

fixed percentage of annual discharge to a more complex matching of historical

frequencies, durations, timing, and rate of change of discharges. In South Africa,

environmental water requirement (EWR) as laid out in the National Water Act (Act no.

36 of 1998), refers to the amount of water, both quantity, and quality, required to protect

the aquatic ecosystems is released (Mantel 2010). In most of the developing countries in

Asia and Africa, the development of irrigation to alleviate hunger and poverty has

primarily been the dominant goal. Because of this, there is little use of EWR at the policy

level, which has led to an overdraft of rivers and aquifers and significant ecological

degradation (Liu et al. 2005).

In Rajasthan State, the policy is not being strictly enforced to ensure the MEF.

However, provision to release water in downstream of irrigation projects has been

mentioned in subsection 1.4.9 of State Water Policy of Rajasthan (2010). Regulatory

reservoirs could be used to mimic natural flows that are needed for ecosystem health.

Matteau et al. (2009) as cited by Kim et al. (2012) mentioned a new paradigm, the

“natural flow regime” concept, was developed in the 1990s for the ecological restoration


36

of rivers altered by anthropogenic activities. Liu et al. (2005) mentioned in their study

that in the late 1990s, the Chinese government initialized activities for the ecological

restoration of the region, including annual flow release to downstream by administrative

order. In particular, research on the determination of environmental flow requirement and

livestock carrying capacity will be important for decision-making support in the

sustainable development of the region. In these situations drinking water needs become

the first priority and even agricultural releases have to be curtailed. Danil‟Yan et al.

(2006) and Lany et al. (2012) reaffirmed that, a critical moment for the assessment of the

admissible water withdrawal is the assessment of the quantitative values of the

environmental runoff (release) of the small rivers, i.e. the minimum runoff that should

persist in the river permanently for the preservation of the environmental equilibrium

within its watershed and prevention of degradation of aquatic and riverbank ecosystem.

Yildirim et al. (2011) have pointed out that besides the scientific and ecological points of

view, knowledge of the size and shape of lake level changes has social effects because the

lakes‟ resources support the economy of communities settled around them. The

environmental health of the Big Bend National Park is affected by the quantity, quality,

and timing of the Rio Grande stream flows (Solis et al. 2011). By the environmental

release is meant the flow of a regulated river, which ensures the reproduction and

functioning of aquatic and on-shore ecosystems in the lower pool of water works (Frevert

et al. 2006, Denim 2010). The forest is an intricate device for catching, holding, using and

recycling water. The forest wealth must be preserved and protected and expanded through

planned large-scale plantation (Kar D. 2011). In this study MEF has been discussed in

detail looking to the importance of this parameter for sustainability of bio-diversity.

Presently in Rajasthan state this phenomenon is not being followed and adhered.

2.11 Water Resources Planning and Management

Water management is the branch of science and technology covering the account, studies,

use and conservation of water resources as well as control of adverse effect of water , OR

a sphere of activities responsible for water resources management with a view to meet the

demands of population and national economy for water, to ensure rational use of water

resources and their protection from pollution and depletion, to ensure operation of water

management schemes, as well as to prevent and eliminate the adverse effect of water


37

(Demin 2010). Human activities have played a conclusive role in the water cycle. The

management of decreasing water resources, as a result of the climate changes within the

Mediterranean region, is challenged in particular, as climate change coincides with high

development pressure, increasing population, and high agricultural demands. Effective

measures to cope with long term, drought and water scarcity are limited and difficult to

implement due to a variety of stakeholders involved and lack of adequate means to

negotiate new policies (Iglesias et al. 2007). Without water survival of mankind is

impossible. Therefore, water resource plans and management policies are essentially be

adopted to manage this scarce resource with maintaining the environmental sustainability.

Water allocation under conditions of water scarcity is looming issue that threatens the

economic well-being and quality of life of many of the world‟s people. Mexico has some

similarity with Rajasthan state viz. Arid to a semi-arid climate with an average rainfall of

527 mm/year and a mean temperature of 22oC. Water scarcity issues in the basin were

highlighted during the 1994-2003 droughts when reservoir storage dropped below 20% of

capacity, and agricultural planting was severely curtailed. The main crop within the basin

is wheat. 13% of the Mexican federal budget was allocated to subsidies in agriculture.

Water Users Associations (WUA) has been transferred the control of water to a local

group of farmers. Crop prices and crop costs are derived from the database compiled by

the Mexican government. Competition among water use sectors is expected to increase in

the next several decades, as the urban population increases and variability in precipitation

may increase due to climate change. Historically, the majority of the flow in the basin has

been allocated to human uses without regards to ecological impacts (Hernandez et al.

2011). The main difference between the two states is attributed to the Mexican people,

who have a willingness to pay (WTP) for uses of water. World Bank (2001), Wang

(2003), and Cai (2008) as cited by Han et al. (2012) defines, Water resources

management efforts have been shifted from engineering (e.g. Dam and water channel

construction) to economic/resource based water management approach (Han et al. 2012).

Kar D. (2011) pointed out that India‟s water crisis, particularly in the cities is

mainly due to poor management and water management in the urban areas of India is said

to be the worst in the world. The UN Charter of the year 2002 accepts the Rights to Safe

Water with respect to safety, affordability and accessibility as a basic human right. Water


38

is the most precious natural resource and is a critical element in any development

planning. We should aim at providing adequate water supply at a suitable pressure for

various uses such as domestic, irrigation, drinking, sanitation, industrial, commercial,

construction and other uses and at the same time protecting the environment. Irrigation

activities in India alone consume about 80% of available water as the practices adopted

are outdated and largely wasteful. Improved methods of irrigation are available today

whereby the same amount of crops can be grown using only about 20% of the irrigation

water presently being used. So, water conservation is an urgent necessity with enough

storage by rainwater harvesting, economizing on water use, reducing waste to the

minimum, recycling, and reusing of used water (Kar D. 2011). Water savings from

agriculture are considered the most critical measure for long-term, sustainable

management of the watershed (Lui et al. 2005; Sun et al. 2012). The major components of

the water management system should include assessment and optimization of supply,

demand management, participatory and transparent management operating system,

market-based regulatory mechanism, and combining authority with responsibility taking

care of ecological sustainability. Zajac (1995) as cited by Draper (2008) described two

important characterizations: Rule Fairness (or Procedural Justice) and Outcome Fairness

(or Distributive Justice) for effective water management. Sustainable plans and policies

are more likely to be those that reflect a consensus, to the extent possible, among all

impacted stakeholders. Compromises are often necessary for participatory water

resources planning and management (Takayanagi et al. 2011). The various demands for

water are all essential to our way of life: economic growth and prosperity, agriculture, and

improved quality of life.

In many river basins in the western United States, water management agencies are

charged with tracking the legal ownership of water in storage and water delivered to users

who have legal rights to the water. An independent panel of technical experts meets on a

regular basis to review the progress of the program and provide recommendations for

future directions. The Stochastic analysis, modeling, and simulation (SAMS) program can

also be very useful for policy planning studies. For water resource planning and

management, we have to consider our stakeholders and their interest; our legal and

political constraints; and technical information and knowledge, which has also been


39

described by Liu et al. (2005); Frevert et al. (2006); Lemeshko (2011). Kar D. (2011)

emphasizes to use benchmarking techniques to improve the operating efficiency of the

entire distribution system. Some of the possible solutions to the problem of safe water as

suggested by Kar D. (2011) are: Water conservation; Technological up-gradation;

Innovation; Making more water available by finding new sources; Recycling and Reusing

used water; Equitable access and distribution; Desalination of sea water; Eliminating

contamination in storage and distribution network; Growing awareness amongst the

common people and developing a change in mindset; Privatization in selected areas;

Decentralization and reduce Government intervention. The long term holistic planning is

a must but is becoming all the more difficult due to uncertainties in forecasting the

oncoming weather pattern. Permeable Pavement System (PPS), which are sustainable and

cost effective processes, is suitable for a wide variety of residential, commercial and

institutional applications. Infiltration supports groundwater recharge, decrease

groundwater salinity, allows smaller diameters for sewers (resulting in cost reduction),

and improves the water quality of receiving waters. Therefore, Best Management

Practices (BMP‟s) based on infiltration are the foundation of many low-impact

development and green infrastructure practices (Chahar et al. 2012). Groundwater

banking is one approach leading to better water management. Deficits occur when the

bank is empty, and the other two sources are unable to satisfy the demand. Municipal and

industrial accounts have the highest priority, and they are guaranteed an amount for each

year. The rest of the users are not guaranteed, and their allocation depends on the water

remaining in their accounts from the previous years. If there is surplus water remaining, it

is allocated to agricultural users, then mining, and finally other uses (Solis et al. 2011).

State Water Policy (2010) of Rajasthan State fixes water use priority for water resources

management and planning purposes:

1. Human drinking water

2. Livestock drinking water

3. Other domestic, commercial and municipal water uses

4. Agriculture

5. Power generation

6. Environmental and ecological


40

7. Industrial

8. Non-consumptive uses, such as cultural, leisure, and tourism uses.

9. Others

All existing problems become even more acute in low-water periods, the large lengths of

which, along with extreme runoff deficiencies have a strong effect on the strategy and

tactics of water management (Danil‟Yan et al. 2006)

2.11.1 Conjunctive Use of Surface and Subsurface Water

In watersheds with stressed water balance, as well as with the purpose to enhance the

utilization efficiency of water resources, admissible runoff withdrawals can be estimated

based on a combined or joint use of surface and subsurface water resources. It is highly

desirable that the groundwater reserves exhausted during the low-flow period will be

replenished almost completely in the subsequent high-flow period before the following

low-water period (Danil‟Yan et al. 2006). Sinovic and Li (2003) as cited by Chang et al.

(2011) represented that the climate changes caused by global warming have made

hydrological conditions increasingly unstable spatially and temporally. Because surface

water resources are highly related to climate changes global warming increases the risk of

water deficits. Tsur (1990) as cited by Chang et al. (2011) states that groundwater

provides a more stable water resource, and rich groundwater areas play a vital role in

regional water resources. Therefore, the conjunctive use of surface and subsurface water

can enhance the water supply reliability.

It is the best way to manage the scarce water resource by conjunctive use of

surface and subsurface water. Simulation results indicate that conjunctive use with

artificial recharge indeed reduces the frequency of extreme water shortages (Valazquez et

al. 2006; Yates et al. 2009; Chang et al. 2011; Bolgov et al. 2012). Solis et al. (2011)

developed water evaluation and planning system (WEAP) software to evaluate the

groundwater banking policy. In lieu groundwater banking is a conjunctive water

allocation policy applicable to water users supplied from surface water and groundwater

sources. Chang et al. (2011) and Oel et al. (2011) infers from the study that conjunctive-

use with artificial recharge indeed reduces the frequency of extreme water shortages.


41

Groundwater and surface water are in a continuous dynamic interaction. They

interact in a variety of physiographic and climatic landscapes. Thus, alteration or

contamination of one commonly affects the other. For instance, pumping of groundwater

can deplete the quantity of water in streams, lakes or wetlands; and withdrawal from

surface water bodies can degrade groundwater quality and vice versa. It is true that the

interaction between surface water and groundwater system is very complex, and it is a

very difficult task to simulate the interaction. Quantity and quality of one system

eventually affect the other. Streams interact with many types of in streams and subsurface

storage zones with varying sizes and time scales of exchange. Lakes are surface water

systems which impound water and either form a hydrological link between groundwater

up gradient and down gradient to the lake or uniformly recharge or discharge into

groundwater. Lakes interact with groundwater in a complex fashion, allowing for many

different flow regimes (Hassanzadeh et al. 2012; Nield et al. 1994 as cited by Bobba 2012

found 39 distinct flow regimes) and seepage patterns. Stable isotopes have been used

fairly extensively in understanding lake-groundwater interactions (Bobba 2012).

2.11.2 River Basin Management

River basin management is as varied as each basin (Teasley and McKinney 2011). Frevert

et al. (2006) pointed out that efficient management of resources is critical, particularly as

the competing demands for water in the river system increase. Improved watershed and

river system operational decision-making is an integral part of improving efficient use of

our limited resources, and decision support systems are necessary. Water management

must respect political boundaries, but effective planning and management requires

consideration of hydrologic realities that water functions in a watershed and river basin

framework. Unfortunately, there is no incentive for the marketplace to engage in river

basin planning (Draper 2008).

Walker (2006) shown that many political factors impacted the development of the

Colorado River Basin Water Agreement (CRBWA). These political factors included

politicians, political agencies, legislation, and political pressure groups/lobbyists. Politics

will always be present in government; there is no way to separate completely it from the

administration. Recommendations have been formulated that will minimize the impact of

politics on the agreement and assist in the attainment of the goals of CRBWA (equitable


42

water distribution and removal of interstate controversy). Due to record population

growth and drought, river basin management becomes very significant. The United State

Bureau of Reclamation„s mission was to manage, develop, and protect water and related

resources in an environmentally and economically sound manner in the interest of the

American public. The present use of water resources should take account of future

generations. It is fair to say that the number of stakeholders involved in the negotiations

probably helped to reduce the nature of any political impact that occurred. A Political

process can have a positive impact when knowledgeable, fair politicians are involved in

the process and are willing to reach consensus, this can be a very positive process and the

impacts can be beneficial to all involved. It was mentioned that there was excitement

about the drought because, in the long run, we will have learned to utilize better this

resource that we have all taken for granted. History has shown that we cannot continue to

take and waste without consequence. The current drought is teaching us to be more

responsible which will make us successful when we are challenged by any real „doom and

gloom‟ and to that point of consequence. For a semiarid state like Rajasthan, it is essential

to conserve every drop of rain water. River basin management by micro watershed

planning should be done and finally any surplus water of the basin, leaving the MEF,

should be stored at the terminal dam of the basin. In Rajasthan State, Isarda dam of

capacity 305 MCM is sanctioned on the same principal as a terminal dam of Banas Basin.

2.11.3 Small WHS and Large Dams

In water resources management, a dam is useful for ensuring a stable water supply and

mitigating floods. Semiarid river basins like Rajasthan often rely on reservoirs for water

supply. To ensure the availability of water near small habitations, public need to construct

small Water Harvesting Structures (WHS). There is a great debate on the issue of small

v/s large dams all over the world. Small reservoirs get silted up much faster than the large

dams. Krol et al. (2011) explain that small reservoirs may impact on large-scale water

availability both by enhancing availability in a distributed sense and by subtracting water

for large downstream user communities, e.g. served by large reservoirs. Large scale

distributed water availability supplied by storage in small reservoirs is more vulnerable

than large scale availability towards downstream, supplied by storage in large scale

strategic reservoirs. Upstream small reservoirs would enhance the climate change effect


43

on strategic reservoirs. There is also a debate on „Dams or No Dams‟. The greatest

opposition which dam builders around the world face is on environmental, financial,

economic, and human rights front, whereas the proponents of large dam push their agenda

on the ground of enhanced food and drinking water security, hydropower generation, and

flood control. Large dams are important for human development and economic growth.

(Shah and Kumar 2008). When people are allowed to construct small WHS in a

decentralized manner over a large catchment, then it shows a tremendous impact on the

inflow to the large dams downstream. A study conducted by Mantel et al. (2010) proved

that the cumulative effect of a high number of small dams is impacting the quality and

quantity of waters in South African rivers and that these impacts need to be systematically

incorporated into the monitoring protocol of the environmental water requirements. South

Africa receives 497 mm of rainfall against a global average of 860 mm. High potential

evaporation rates (1100 to 3000 mm), leading to a value of mean annual runoff to mean

annual precipitation of only 8.6%, similar to Australia (9.8%) but much lower than other

parts of the globe (e.g. Canada 65.7%). Interbasin transfers and large dams have been

emphasized as solutions to improve water security in the past in South Africa. Regional

and global surveys have hypothesized that small dams can have a significant impact on

river systems because of their large number and the total area covered. Numbers of farm

dams (i.e. Small dams that do not require a license) are not accounted in the inventory of

dams. There is a paucity of analyses of cumulative impacts of small dams and weirs on

quantity and quality of rivers and river ecosystems. It is widely recognized that dams

usually have negative impacts on the downstream flow regime required by riverine

ecosystem (Liu et al. 2005). Recently, the population of the Indus delta area has carried

out demonstrations of protest under the slogan “No more dams in the Indus! Protect the

Indus delta (Kravtsova et al. 2009). The World Commission on Dams has emphasized the

wide range problems associated with dams (Sun et al. 2012). However, the importance of

dams for providing water for human use in a semi-arid country cannot be underestimated

(Mantel et al. 2010).

Kar D. (2011) suggests preserving and protecting wetlands and new ponds, lakes

and reservoirs created in a planned manner. This helps in aquaculture, fishery and raising

the sub-soil water table apart from generating employment for the local people. Since


44

annual evaporation losses in the state are to the tune of 1.8m, therefore, the proposal of

large dams is preferred for any virgin catchment remaining in the state. However, after

considering the recharge strata, consent of the local village government and ecological

and forest aspects, small dams in the form of WHS are also constructed in the state. A

large terminal reservoir can be made at low dependability to store the surplus water of the

basin as cited by Shah and Kumar (2008) that in Narmada basin, the Sardar Sarovar

reservoir being the terminal reservoir, it can receive all surplus flows from the reservoirs

upstream.

2.11.4 Micro Watershed Planning

To ameliorate the situation of water scarcity and environmental degradation, watershed

restoration is being contemplated for the Western Inner Mongolia region as the

fundamental element of a new ecosystem management philosophy (Liu et al. 2005). The

popularity of watershed models is no surprise due to their ability to provide holistic

interpretation of how natural systems that are driven by hydrologic processes are

impacted by anthropogenic disturbances (Migliaccio and Srivastava 2007). River basin

and watershed planning and management have been acknowledged as one of the most

important techniques to be used in managing water (Draper 2008). It was felt (Kim et al.

2012) to identify the effects of flow regulation on dams on the downstream flow regimes

of a basin. In Rajasthan, micro watershed planning is being adhered before sanctioning

any new surface water storage project.

2.11.5 IWRM and INRM

Integrated water resources management is simply the management of available water for

allocation to stakeholders keeping in view the environmental sustainability. Integrated

water resources planning and management typically involve the considerations of

multiple economic, environmental, social, financial, institutional; and technical impact

and the participation of multiple interest groups and stakeholders (Liu et al. 2005;

Selyutin et al. 2009; State Water Policy 2010 (2.0); Takayanagi et al. 2011; Maheshwari

et al. 2012; Nayak 2014). Singh (1995) as cited by Wang et al. (2008) stated that because

of increasing demand for water in the face of deteriorating water quality, it is essential to

adopt an integrated water resources management (IWRM). Water allocation models


45

possess common deficiencies, they only consider quantity constraints, and do not include

water quality constraints. Gupta and Zaag (2008) as cited by Karamouz et al. (2010) have

assessed the interbasin water transfers from a multidisciplinary perspective and attempted

to answer whether such transfers are compatible with the concept of integrated water

resources management and the criteria for assessing such transfers. We should consider

sustainability and how we develop and manage our natural and cultural resources to

benefit both us and future generations. It is essential to plan and manage water and

environmental resources with adaptable, robust and integrated approaches (Saito et al.

2012). Integrated watershed modeling approaches provide methods to link groundwater

and surface water systems in the model hydrologic domain. The development of the

integrated model is at present hindered by the lack of efficient algorithms for coupling

both water masses whose time steps are very disparate (Bobba 2012). Winter (1999) as

cited by Bobba (2012) explained that several factors have a significant effect on

groundwater-surface water interactions. These can be enumerated as (a) regional

physiographical framework; (b) local water table configuration and geologic

characteristics of surface water beds, such as the distribution of sediment types possessing

different hydraulic conductivities and orientation of sediment particles; and (c) climatic

effects on seepage distribution in surface water beds.

2.11.6 Transboundary Water Transfer and Water Sharing

Sharing water across political boundaries is not an unusual phenomenon. There are 268

transboundary river basins worldwide. Two hundred fifty major rivers are shared between

and among two or more nations. Over fifty rivers are shared by three or more nations,

with the Danube being shared by 13 riparian countries. These basins cover almost two-

thirds of the global landmass; forty percent (40%) of the world's population depends on

these shared river basins for water (Draper 2007a). Several writers (Draper 2007b; Draper

and Kundell 2007; Eleftheriadou and Mylopoulos 2008; Wang et al. 2008; Karamouz et

al. 2010; State Water Policy 2010 (1.5); Teasley and McKinney 2011; Shiklomanov et al.

2011; Bhandari and Liebe 2012; Lany et al. 2012) stated that transboundary water sharing

agreements provide a solution for water scarce areas and management of transboundary

water resources has been the focus of many studies due to the observed water stress all

over the globe combined with the continually increasing water demand. Teasley and


46

McKinney (2011) shows the usefulness of the agreement on the allocation of water and

energy resources of the Syr Darya basin considering transboundary cooperation and

benefits sharing using the river basin model and game theory concept. Water-sharing

agreements increase benefits to all countries in the basin if they follow the agreement.

The shapely allocation provides each country with increased economic benefits. Likely

actions of the upstream countries have to be considered, and provision of compensatory

payment for compliance with the treaty should be made. Transboundary water allocation

and sharing in river basins that border or pass through two or more countries can be

difficult because they are subject to politics, laws, and regulations. It is sometimes

observed that trade policies, military affairs, immigration, and a host of other issues might

come into play in water negotiations (Teasley and McKinney 2011). The effectiveness of

any law, regulation, contract, or legally enforceable agreement, regardless of the subject

matter, depends on how effectively it is administered and how rigorously its provisions

are enforced. Without an effective institutional framework, the parties will spend much of

their time and resources in dispute resolution rather than effective water management.

The institutional provisions must provide for effective cooperation, coordination, and

communication. They can ensure that conflicts are resolved in a timely and cost-effective

manner. These institutional provisions will involve far more than just engineering

management of the water resources in question. They must also involve establishing

procedures to manage the complicated coordination among a number a private and public

institutions, addressing critical social issues, responding to complex environmental and

ecological sustainability needs (Draper 2007b). International water laws (Comair et al.

(2012) such as Helsinki Rules on the uses of the waters of international rivers of 1966

(ILA 1966) and the 1997 United Nations Convention on the law of the non-navigational

uses of international watercourses, mention main components to be considered for water

allocations in international river basins. Eleftheriadou and Mylopoulos (2008) used game

theoretical approach (negotiation analysis or science of strategy) and multi-criterion

decision analysis (MCDA) for conflict resolution in transboundary water resources. At

the point of Nash equilibrium all players profit the most (i.e. Have high payoffs), taking

into account all possible moves of the counter-players. An early study of Rogers (1969)

as cited by Eleftheriadou and Mylopoulos (2008) on the cooperation of India and Pakistan


47

in the Lower Ganges case is one of the first attempts to implement the game theory in the

sector of water resources management. Russia has 37 large water management systems,

which are used for interbasin river flow redistribution from water abundant areas to water

deficit areas. The total length of water transfer canals on the territory of Russia exceeds

3000 km, the volume of transferring flow approximates 17 BCM.

Transferring water from an area may cause a variety of negative impacts, social

and environmental impacts; the water quality simulation model was used to consider

environmental impacts of the water transfer model. In the national arena, water is equity

for all. Equity for those who are in need of water and do not have access to water and

those who have the water rights and may have a surplus that is wasted in a variety of

ways. When water is intended to be used in another basin, water rights could be traded for

financial resources. Temporal and spatial distribution of water resources pose a challenge

to the demand and supply management of this scarce resource. The interbasin water

transfer project is an alternative to balance the non-uniform temporal and spatial

distribution of water resources and water demand, especially in arid and semiarid regions,

but it should be environmentally and economically justified. Water transfer leads to

pollution of sending river due to withdrawal water and wastewater discharge as already

taking place (Karamouz et al. 2010). Feng et al. (2007) as cited by Karamouz et al. (2010)

developed a decision support system (DSS) for accessing the social-economic impact of

china‟s South to North water transfer project. To determine the operating rules, a KNN

model is developed to be used in real time operation. It is cited by Shah and Kumar

(2008) that bulk water transfer in China from water rich Yangtze River basin to seven

provinces, including Beijing for drinking water needs and the Yellow River for ecological

needs.

In Rajasthan, for the sake of state water resources planning and management,

following transboundary water transfer proposal are under investigation:

Transfer of 355 MCM surplus water by the interlinking of Chambal and Brahmani

Rivers to Bisalpur dam to augment the drinking water supply of Jaipur city and

other nearby towns.

Transfer of 1077 MCM surplus water by interlinking of Parwati and Kalisindh

Rivers to Banas, Gambhir and Parbati Rivers.


48

Transfer of 328 MCM surplus water by interlinking of Mej River to Ramgarh and

en route dams.

Transfer of 107 MCM surplus water by interlinking of Chambal River to

Jaisamand dam of Alwar district.

Parwati-Kalisindh-Chambal link scheme under National River Linking Project to

transfer 1085 MCM surplus water.

Sharda-Yamuna Link, Yamuna-Rajasthan Link, and Rajasthan-Sabarmati Link are

also proposed to be investigated for transboundary water transfer from outside

surplus water to the state.

2.11.7 Water Pricing, Water Market, Water Trading, and Water Privatization

Water cannot be considered as a common article of merchandise subject to purchase and

sell. This is an inherited good, which should be preserved, protected, and treated

appropriately. Surface and subsurface waters are regarded as natural resources, renewable

in principle (Khaustov and Redina 2011). A major problem of public allocation is that no

mechanism exists for public water allocation to shift water from older, lower-valued uses

to newer, higher-valued uses. The artificially low cost of water, due to government

subsidies, certainly lowers the incentive to conserve and may result in a waste of water.

The unwillingness of the public to pay for essential infrastructure maintenance lead to

sacrifice long-term vision and our political leaders fail to demonstrate that long-term

vision and tend to pander to short-term, self-interests (Saito et al. 2012). The Dublin

statement that water is an economic good prompted the World Bank, among others, to

propose the privatization of a source water; that nations and states introduce tradable

property rights to water in order to "increase the productivity of water use, improve

operations and maintenance, stimulate private investment and economic growth, reduce

water conflicts, rationalize ongoing and future irrigation development, and free up

government resources. Under such conditions, demand and supply forces will determine

the quantities to be traded and the unit price of the commodity in this market. A sense of

“willingness to pay” is developed in the society. Market institution models have been

implemented in Chile, Canada, Australia, Western United States and Mexico. Draper

(2008) advocated the water privatization stating “Although the causes of water scarcity

are varied, many assert the inefficiencies and failures of water allocation by the public


49

sector are to blame. One solution offered by proponents is to privatize the water

allocation process, thereby allowing the efficiencies of the marketplace to resolve these

issues. While the legal right to the use of water may be placed in the hands of the private

sector, these rights along with core functions must be closely controlled and managed by

public institutions. The central theme of some books, professional journals, and magazine

articles on water scarcity is that inefficient water management is an inherent consequence

of the inefficiencies and failures of water management by the public sector. The solution

in their views is to privatize water allocation. The scarce water resource will,

consequently, be used for the “highest and best” uses. Freshwater is a finite and

vulnerable resource; essential to sustain life, development, and the environment. Water

has an economic value in all its competing uses and should be recognized as an economic

good. Mechanisms for water allocation exist across a wide continuum of strategies. At

one end of the continuum is the public allocation and the other end is private allocation”.

Some authors as cited by Draper (2008) have suggested that water can be managed in

more productive and efficient manner when treated as a “tradable standardized

commodity” rather than as a product of engineering or an integral part of nature. The

argument for tradable rights to water is based on the principles of the economics of

scarcity and neoclassical economic theory. Australian‟s Water Policy Agreement in 1994

endorsed the adoption of tradable water entitlements and clarification of property rights in

water. Water as a product requires large expenses for its transportation in comparison

with its use. The increase in the cost of a resource is undoubtedly an incentive to its

economy and more efficient use though the efficiency of this incentive is essentially

dependent on the elasticity of demand with respect to cost (Danil'yan 2005).

Water intra stakeholder transfer and inter-stakeholder trading will lead to an

increase in the annual net benefit of the basin. The cooperation of all stakeholders can

produce the largest overall gain; the government may need to regulate the water market

and provide information to guide water trading to achieve optimal allocation in a river

basin (Wang et al. 2008). Good quality water at adequate pressure cannot be supplied free

of cost round the clock. The consumers need to bear a part of the water cost, hence; water

pricing has to be introduced, and this calls for a change in the mindset of the Indian

people. Introducing a water pricing structure would be effective in motivating water


50

conservation (Kar D. 2011). The primary responsibility for ensuring equitable and

sustainable water resource management rests with the government. Public responsibility

includes the task to set and enforce stable and transparent rules that enable all water users

to gain equitable access to, and make use of, water. One of the most serious disadvantages

of exclusively private allocation is to consider the effect of the transaction on the third

party, public, or environmental interests. A private allocation system will impede basin-

wide planning and management of public institutions as well as environmental and

ecological protection needed to sustain public health. A myriad of potential water

allocation alternatives exist in the continuum between these two poles, and a logical

choice is a form of public-private allocation that retains the best of both systems and must

involve government oversight and controls (Draper 2008). Out of the important problems

of water supply is the development of an economic mechanism of chargeable water use,

which will dramatically reduce water losses and improve the reliability of water supply

(Demin 2005). Models that integrate hydrology with economic, environmental, and

institutional aspects into a coherent analytical framework can be useful for managing

water resources at the basin level. Incorporating the value of water through economic

analyses can provide critical information for decision about the efficient and equitable

allocation of water among competitive users, efficient and equitable infrastructure

investment in the water sector, and the design of appropriate economic instruments, such

as pricing schemes and water trading markets ( Cai and Wang 2006, Hernandez et al.

2011, Han et al. 2012). Policy for water pricing, water metering, and water tariffs has

been discussed in Chapter 8 of State Water Policy (2010). Water scarcity and ability to

pay are the only current variables for calculating water abstraction charges for each

province. 1998 Water Law implemented water abstraction charges nationwide in China,

and the law was restructured in 2002 to reduce bureaucracy.

2.11.8 Rainwater Harvesting (RWH)

Rainwater is perhaps, the best source of fresh water. It comes only during the monsoon

period and, if not conserved this fresh water may not be available for the rest of the year.

Benefits of RWH include a rise in the groundwater table, thus improving water quality

and yield of aquifers, the salinity of groundwater will reduce augmentation of water

supply system and sustainability of drinking water sources. Rainwater harvesting is useful


51

for effective water conservation and management. The US western states could no longer

afford to continue “wasting” its waste water. There is a lot of interest among the

California, Los Angeles, Colorado, Phoenix, Arizona and some other western cities of

USA in making use of their treated wastewater. Reclaimed water is an uninterrupted

supply. That is why it has become a powerful incentive in the California area for

reclaiming and reusing the wastewater (Kar D. 2011). In Rajasthan, it is made

compulsory for the owners of land of size more than 200 sqm by the Government to store

the rain water through RWH.

Makropoulos and Butler (2010) advocated the distributed water infrastructure for

sustainable communities saying that “Distributed water infrastructure (located at

community or household level) is relatively untried and unproven, compared with

technologies for managing urban water at higher (e.g. Regional) level. Decentralized

options are defined as those being applied at the development level (for example, up to

5000 households). This includes in-house options, water saving devices, conventional,

novel technologies. Both local rainwater and local groundwater are collected in this case

for both non-potable and potable use. Water is treated at the point of use for potable

applications. Bottled water is used to back-up the potable supply when necessary. The

rainwater harvesting system is also used for stormwater management through the

existence of sufficient storage. It is assumed that all water needs (including potable) are

met by rainwater harvesting (and/or local groundwater), with any deficit met by bottled

water.

2.11.9 Reuse and Recycle of Waste Water

As cited by Liu et al. (2005) and Saito et al. (2012), we should treat the wastewater

generating from apartments and office buildings on the site in a cost effective way,

eliminating the need for sewers in urban areas. Makropoulos and Butler (2010) said: "The

amount of waste water produced is assumed to be 94% of the water consumed, and

energy use is associated with water treatment (assumed to be 700 kWh/ML) and waste

water treatment (assumed to be 820 kWh/ML)." Relatively unconventional schemes such

as desalination and effluent re-use to meet the temporary seasonal increase in demand

(critical periods) are essential for the regional solution in the south east of England (Lany


52

et al. 2008). Shiklomanov et al. (2011) insisted on recycling of water for an effective

water management.

2.11.10 Water balance

ET toolbox estimates the losses (Frevert et al. 2006). The purpose of studying the water

balance is to determine whether the inputs (rainfall/inflow) balance the outputs

(evaporation, outflows and changes in storage) (Yildirim et al. 2010, Baynes et al. 2011).

Annual water balance components of the Indus are as follows: the precipitation is 568

mm, and the runoff at the mouth is 98 mm. Therefore, evaporation accounts for 470 mm.

The runoff coefficient is 0.17. The potential evaporation in the Indus Delta is very high,

of the order of 1750-2000 mm/year. Taking into account that the Indus runoff has

abruptly dropped, and the area of the active delta has declined the estimate of the water

balance of the Indus Delta needs refinement (Kravtsova et al. 2009). The potential

evaporation in the Rajasthan state is also very high, of the order of 1820 mm/year

(Bisalpur Report-1991). According to an assessment of Dourte et al. (2012) annually,

about 70% of rainfall infiltrates the soil, and about 11% of rainfall infiltration becomes

groundwater recharge in the region near Hyderabad. For the purpose of applying

hydrological models for water balance analysis and describing hydrological processes,

daily rainfall data do not provide sufficient rainfall inputs (Alfa et al. 2011). At present,

water used in agriculture accounts for 66.7% of total water use in China, most of which is

used for irrigation. It is the biggest factor affecting the basin water cycle (Sang et al.

2010). The WASA model developed mostly in the Brazilian state, describes the water

cycle in a river basin, simulating the water balance of soils and water reservoirs,

generation of runoff and routing of flow through the river network, in which the

reservoirs are located (Krol et al. 2011). Chunale et al. (2014b) found 23.47% of total

seasonal rainfall as total runoff after satisfying all losses like canopy interceptions and

surface depressions (10.64%), soil ET (19.38%) and percolation to upper GW layer

(46.55%) in the Sub-Montane zone of Maharashtra. According to the results of the study

conducted by Chunale et al. (2014b) in Maharashtra, the storage capacities of canopy

vegetation and surface depression vary from 0.75 to 1.80 mm and 0.85 to 1.90 mm

respectively, percolation rates of soil and upper ground water layer from 0.9 to 1.55

mm/hr and 0.80 to 1.39 mm/hr, respectively, and the storage capacity and storage


53

coefficient of upper GW layer vary from 20.00 to 23.90 mm and 2.00 to 2.50 h

respectively (Chunale 2014b). Nayak (2014) used Thornthwaite and Mather model to

compute the total volumetric runoff through the water balance of the Bina River

watershed. Nayak (2014) further explains that hydrological model includes subroutines

for the most significant hydrological processes, such as precipitation at different

locations, soil moisture dynamics, evapotranspiration, recharge of ground water, runoff

generation, and routing in reservoirs and rivers. Most runoff models are based on water

balance, using precipitation as a deriving variable and calculating the quantities directed

as runoff, R, from the water balance equation.

SEPR (2.4)

Runoff= Precipitation- PET- Various storage terms

2.11.11 Water use efficiency

In Rajasthan, to improve the water use efficiency, various programs have been undertaken

like water auditing and benchmarking, repair and rehabilitation of dams and their canals,

lining of unlined reaches of canals, formation of water users associations, formation of

community participation units, and state partnership program under European

Commission for reforms in water sector etc., some of the recommendations have been

made by several researchers (Liu et al. 2005, Gurjar 2011). The major share of water use

is attributed to irrigation requirements. The basis of agricultural water management

involves the replenishment of irrigation water into the root zone of soil that lost due to

evapotranspiration. Evapotranspiration includes evaporation from the surface and

transpiration of water from the plant stomatal activities (Kumar et al. 2014). A Study

done by Han et al. (2012) shows that many opportunities exist to improve water use

efficiency, particularly through agricultural irrigation technology and urban water

conservation efforts. We need technologies that will enable the maximum efficiency in

water consumption in all fields of its use (Danil‟Yan et al. 2005). Correct assessment of

water losses from canal is vital for proper water management system (Akkuzu 2011)

2.12 Summary

Water is the most critical element to human survival and progress. Water is essential for

all economic activities and prosperity of mankind. With the dawn of 21st Century, world


54

stared experiencing the consequences of water scarcity. Draper (2007) stated that in some

areas, water scarcity can be attributed primarily to an arid climate and the lack of

sufficient rainfall. In other areas, even where sufficient source water is available, water

scarcity may still emerge, for a variety of reasons including, many assert misguided

government policies, ineffective public management, or inequitable distribution. Water

scarcity caused by government inefficiencies, ineffectiveness, neglect or negligence has

been described as a “water governance” crisis (UN 2002; World Water Commission

1999; as cited by Draper 2008). To tackle the water scarcity problem and to manage the

supply and demand of water, it is essential to take administrative commands based on

nationwide interest and the interest of individual branches and republics are of secondary

significance (Petrov and Normatov 2010). The reasons for water scarcity are many and

vary from one place to other (Carrasco et al. 2012).

Decreasing water levels of the dams, lakes, and reservoirs and increasing water

stress is being observed in different parts of the world. Excessive ground water

withdrawal during low flow periods for human demands will continue to affect river

runoff until the complete filling of the depression cone that has formed due to water

withdrawal (Danil‟yan et al. 2006). The analysis of rainfall and runoff data revealed that

the flow is reduced at Harike. The reasons for this are the over-exploitation of

groundwater. All the ecological and economic benefits of this wetland are going to be

affected due to the declining trend of flow at Harike (Jain et al. 2008). Yates et al. (2009)

inferred that human interventions in the hydrologic cycle have greatly disrupted the

natural hydrology. Runoff in major rivers in China has been decreasing in recent decades.

The attribution of hydrologic variability to increasing water withdrawal for irrigation,

human activity or climate change is a challenging problem and an active research area

(Kravtsova et al. 2009, Wang et al. 2011). The decrease in precipitation and rise in

temperature may result in runoff reduction (Wang et al. 2011). Water stress in

southeastern England is projected to increase over the next 25 years, influenced by factors

such as population growth; demographic change; changes in water use; water

requirements for environmental protection; and, the potential effects of climate variability

and change (Lany et al. 2012). Study results of Hassanzadeh et al. (2012) shows changes

in inflows to Urmia lake due to the climate change and overuse of surface water resources


55

is the main factor and accounts for 65% of the effect, constructing four dams is

responsible for 25% of the problem, and less precipitation on the lake has 10% effect on

decreasing the lake level in the recent years. Study results of Hassanzadeh et al. (2012)

shows that precipitation has least effect and climate change and overuse of surface water

resources has major effect on inflow to Urmia Lake, while this study reveals that

precipitation is the principal component for inflow to the dams of the Rajasthan state.

Various studies revealed different factors to be responsible for declining trend of

inflow and resulting water stress. Excessive ground water withdrawal is observed as main

factor by many researchers. Some researchers found climate change and human activities

(anthropogenic activities) along with population growth as main factors responsible for

decreasing runoff. Most of the researchers have not carried out analysis for all the

climatic factors and also not conducted the sensitivity analysis to prioritize the factors.

This study reveals that rainfall, anthropogenic activities, ground water level, climatic

factors (such as mean of relative humidity, wind speed, and daily max. temp.), and

population growth are major factors responsible for less water inflow to the dams of

Rajsthan state, specifically to Ramgarh and Bisalpur dam. Sensitivity analysis for

identification of principal component has also been carried out for dams of the state as a

whole and specifically for Ramgarh and Bisalpur dams.

Rain-fed agriculture is a complex, diverse and a risk prone ecosystem that

occupies near about 70% of the total cultivable land (180.5 Mha) in India. Out of this

about 20.0 Mha land depend on rainfall for cultivation falls in Rajasthan. As the rainfall

in the state is erratic and uneven, with an average annual rainfall of 531 mm, state need to

use efficient methods of irrigation like drip, sprinkler, greenhouses, and shed net systems,

which is also recommended by several researchers (Liu et al. 2005). Influence of excess

groundwater withdrawal on inflow to the reservoirs has also been explained by Liu et al.

(2005) and Danil'yan et al. (2006). Jain et al. (2008) showed that wetland area of Harike

has reduced approximately 30% over the last 13 years. It was also explained that during

last five-decade percentage groundwater utilization has almost doubled and the surface

water utilization has increased many fold. There are arguments that extensive rice and

wheat growth has encouraged the people to extract more and more groundwater causing


56

the decline in the water table. Danil‟yan et al. (2005) concluded that the problem of water

deficiency is shown to be insoluble without the development of intensive water use,

conservation, and protection. Iglesias et al. (2007) and Tarun (2007) also emphasized that

water saving is the key strategy for overall reduction of societal vulnerability to drought.

Jain et al. (2008) concluded that all the ecological and economic benefits of Harike

wetland are going to be affected due to the declining trend of flow. The impact of

population density on the water resources have been illustrated by many researchers.

With the high population density and its rapid growth in the Central Asia, this has already

resulted in a deficiency in water resources in the region. In the future, this deficiency can

acquire even more acute and crisis forms. Various measures have been proposed for

solving this problem, including the diversion of Siberian River's runoff into Central Asia

or serious evolutionary changes of the type of water consumption, implying a change to a

new national economic strategy and social policy (Petrov and Normatov 2010).

Hassanzadeh et al. (2012) also found some similar results in declining the Urmia Lake's

level. He also cited that with the increasing population, farmlands would increase in the

future. Unfortunately new dams would be constructed in this region. It is necessary to

construct hydrometric stations in near the lake to understand the exact hydrological

behavior. Hassanzadeh et al. (2012) concluded in their study that managing water supply

and irrigation, introducing water market, strict water rights, modifying the farming, public

awareness to conserve water and averting new dams construction in the basin, can help

the lake to keep its current level, furthermore, conveying water from other basins to

satisfy the agricultural demands in this region could be a proper alternative. The declining

water table reduces runoff due to baseflow and hence the inflow to a wetland. Population

growth and demographic and land use changes also creating water scarcity in the

southeast of England (Lany et al. 2012). The terrain of the study area is flat and with the

increased agricultural and developmental activities it is becoming flatter. The flat

topography favours for more infiltration opportunity time leading to considerable entry

from runoff water below the soil surface (Parmar et al. 2014).

The increase in population density of the state is leading to increase in

abstractions in the catchment area of existing dams. These types of abstractions may be in

the form of infrastructural development (residential, institutional, industrial, road network


57

etc.), agricultural development (net crop area, area sown more than once), water

harvesting structures (WHS, check dams, subsurface barrier, up steam dam development,

field confinement, field dams, soil conservation structures etc.), forest development

measures (contour vegetation hedging (CVH), Ditch Cum Bund (DCB) etc. (Appendix-

10b and 11 a to f). Various hydrological methods can be employed to simulate the inflow

to the dams. Various management techniques should be employed to cope up with water

scarcity situations. Stringent policies may be implemented to safeguard the catchment

area of natural streams (Appendix-11) and to manage the demand and supply of this

scarce resource.

The work on rainfall-runoff modeling has been done all around the world a lot.

Decreasing water levels in the reservoirs has been studied in many parts of the world by

various scientists. Scientific research for temporal variation of inflow to the dams of

Rajasthan state specifically for Ramgarh & Bisalpur dams have not been undertaken.

Determination of critical year along with principal component responsible for temporal

variation of inflow has not been determined scientifically earlier. This study

encompasses towards testing the temporal variation of inflow, determination of critical

year, and identification of principal components responsible for less water inflow to the

dams of the state and specifically to the Ramgarh and Bisalpur dams. Sequential Cluster

Analysis found to be very good approach for determination of critical year, understood

through this literature, will help in determination of critical year. Cosine Amplitude

Method of sensitivity analysis is also very helpful method for determination of principal

component responsible for temporal variation of inflow to the dams. The steps taken

forward all over the globe for water resources planning and management have been

discussed in detail. This may help in finding some of the effective solutions for the

problem of less water inflow to the dams of the state and to combat with the resulting

water crisis.


58

3 MATERIALS AND METHODS

3.1 Introduction

3.2 Materials: Data Collection

3.2.1 Inflow Data

3.2.2 Rainfall Data

3.2.3 Climate Data

3.2.4 Land Use Land Cover Data

3.2.5 Ground Water Levels

3.2.6 Population Data

3.3 Methodology

3.3.1 Sampling

3.3.2 Inflow Trend Analysis

3.3.3 Formulation and Testing of Hypothesis

3.3.4 Performance Measures and Performance Statistics

3.3.5 Determination of Critical Year

3.3.6 Various Factors, their Trend Analysis, and

Correlation Matrix

3.3.7 Regression Analysis and Determination of Principal

Components

3.3.8 Cosine Amplitude Method of Sensitivity Analysis

and Determination of Principal Components

3.3.9 Multiple Linear Regression (MLR) for generation of

Inflow Equation

3.3.10 Regression Analysis and Determination of Factors


3.4 Methodology Flow Chart

3.5 Underlying Assumptions


59

CHAPTER-3

MATERIALS AND METHODS

3.1 Introduction

It is essential to analyze the different types of data regarding changes in rainfall, climatic

factors, land use land cover, and water inflow to existing dams because there are many

factors affecting the decline in water levels in the dams. Different types of data were

obtained from various sources. There data were used and analyzed using several

techniques in this study. The basic data used in this study includes: (i) rainfall data

(source: Water Resources Department, Rajasthan); (ii) data of water inflow to the dams

(source: Water Resources Department, Rajasthan); (iii) other climate data e.g.

temperature, wind speed, relative humidity fraction, solar radiation (source:

globalweather.swat.tamu.edu); (iv) land use land cover data (source: basic statistics &

agricultural statistics published by Directorate of Economics and Statistics,

bhuvan.nrsc.gov.in of National Remote Sensing Center, glfc.com, envfor.nic.in of

Ministry of Environment and Forest, and iscgm.org of International Steering Committee

for Global Mapping); (v) population data (source: basic statistics published by

Department of Economics and Statistics), (vi) ground water level data (source: ground

water department) and (vii) field studies and results of other relevant studies.

3.2 Materials: Data Collection

For any hydrological analysis, it is necessary to obtain the real-time data of rainfall,

inflow, land use land cover (LULC), and other anthropogenic changes. Various types of

data have been collected from various departments of Government of Rajasthan by

personal visit, purchase, and Right to Information Act-2005. Data related to rainfall and

inflows have been collected from the Water Resources Department (WRD). LULC data

has been collected from the various publications viz. Some facts about Rajasthan, Basic

Statistics of Rajasthan, Statistical Abstract of Rajasthan, Statistical Outlines of various

districts of Rajasthan, Agricultural Statistics of Rajasthan, website of National Remote

Sensing Centre of Government of India (http://bhuvan.nrsc.gov.in) and other related

websites. The consistency of data is maintained to get the reliability.

http://bhuvan.nrsc.gov.in/


60

3.2.1 Inflow Data

Real-time inflow data have been collected from the Water Resources Department (WRD)

of Government of Rajasthan. There are 693 dams under the jurisdiction of WRD. There

are 460 dams in the state, which are having the capacity more than 4.5 MCM. Out of

these 460 dams, there are 115 major and medium dams in 11 river basins, inflow data of

which are used for testing the hypothesis. For health performance study of the dams,

inflow data of 32 dams of Tonk district selected through area sampling has been used in

this study. Detail of basin wise dams in Rajasthan state is shown in Fig. 1.2. Surface

water infrastructure of the state is shown in Table 3.1.

Table 3.1 Surface water infrastructure of the state

S.N. Name of basin No. of dams

1 Total no. of water bodies in the Rajasthan State 72569

2 Small+ Minor+ Medium+ Major Dams 3302

3 Minor+ Medium+ Major Dams 693

4 Medium+ Major Dams 115

5 Major Dams 22

Inflow data of dams of Rajasthan State has been collected from 1990 to 2013 as

per availability of continuous data. For special reference to Bisalpur dam, the simulation

period was set from 1979, when the measurement of inflow to Bisalpur dam site was

started, to 2013, when the measured inflow to the dam is available. Annual mean inflow

to Bisalpur dam is 1170 MCM; however, the minimum inflow decreased to 17 MCM

during the year 2002. For special reference to Ramgarh dam, the simulation period was

set from 1983, when the measurement of inflow to dam site is available, to 2010, when

the measured inflow to the dam significantly changed. Until 2010, the mean annual

inflow to Ramgarh dam was approximately 13.81 MCM; however, the minimum inflow

decreased to 0.0 MCM during 2009. From 2011 and onward, the inflow to the dam

remained to zero even after above average rainfall. Although this decrease represents a

change in the hydrologic properties of the catchment area, the exact cause is unclear.

Therefore, the chosen simulation period was considered reasonable to evaluate the

temporal variation of inflow.


61

3.2.2 Rainfall Data

For analysis of the state, daily precipitation data was obtained for 544 Rain Gauge

Stations (RGS), spread all over the state, from the Water Resources Department for 113

years (1901-2013). Rainfall data for 34 stations are shown in Appendix 2.

For analysis of the Ramgarh dam, daily precipitation data was obtained for 4 Rain

Gauge Stations (RGS), spread within and around the catchment area, from the Water

Resources Department for 32 years (1983-2014). Annual weighted rainfall is computed

using the Thiessen weighting technique as depicted in Fig 3.1. Computation of influence

factor for the 4 RGS is shown in Table 3.2. Average rainfall during the simulation period

(1983-2014) in the catchment area of Ramgarh dam was 551mm.

Table 3.2 Computation of influence factor: Ramgarh

S.N. Name of RGS Area Influenced, Ai (km2) IF

1 Shahpura 408 0.49

2 Amer 67 0.08

3 Jamwa Ramgarh 250 0.30

4 Virat Nagar 107 0.13


62

Fig. 3.1 Thiessen polygon for Ramgarh dam catchment


63

Fig. 3.2 Thiessen polygon for Bisalpur dam catchment


64

For analysis of the Bisalpur dam, daily precipitation data was obtained for 11 Rain

Gauge Stations (RGS), spread within the catchment area, from the Water Resources

Department for 35 years (1979-2013). Annual weighted rainfall is computed using the

Thiessen weighting technique as depicted in Fig 3.2. Average rainfall during the

simulation period in the catchment area of Bisalpur dam was 573 mm.

3.2.3 Climate Data

Five climatic parameters, namely maximum and minimum temperature (oC), relative

humidity (fraction), wind speed (m/sec), solar radiation (MJ/m2) were obtained on the

daily basis for four stations of Ramgarh catchment and 36 stations of Bisalpur catchment

from the SWAT website (http://swat.tamu.edu). For Ramgarh dam catchment, mean

minimum and maximum temperature, wind speed, relative humidity, and solar radiation

during the period (1983-2010) were 33.550C, 17.46

0C, 2.62 m/s, 0.40, and 18.53 MJ/m2,

respectively (Appendix-3). For Bisalpur dam catchment, mean minimum and maximum

temperature, wind speed, relative humidity, and solar radiation during the period (1979-

2013) were 33.670C, 18.95

0C, 2.9 m/s, 0.39, and 19.26 MJ/m2, respectively (Appendix-

4).

3.2.4 LULC Data

Land use/land cover data has been extracted from district wise, year wise various

publications issued by Directorate of Economics and Statistics, Rajasthan. For

Rajasthan State, the LULC (1957-2012) is shown in Table 3.3 and Table 3.4 is showing

specific areas of barren, culturable waste and fallow land having the contributing effect to

runoff, and agriculture, forest, residential areas, roads, mining, etc. having the reducing

effect on runoff.


65

Table 3.3 Land utilization of the Rajasthan state (000 ha)

Year Forest

Area

put to

non-

Ag.

uses Barren &

Un-

culturable

land

Other uncultivated lands Fallow land

Net

area

sown

Total

cropped

area

Area

sown

more

than

once Resi.

roads,

mining,

etc.

Pasture

&

Grazing

land

Miss.

trees

crops

&

grooves

Culturable

waste

Other than

current

fallow<5yr

Current

fallow

(this

yr.)

1 2 3 4 5 6 7 8 9 10 11 12

1957 1438 1090 4912 1380 46 7326 3311 2248 12425 13711 1286

1958 1159 1125 4911 1397 21 7313 3758 2320 12103 12944 841

1959 1146 1168 4896 1526 23 7063 3639 2066 12588 13728 1140

1960 777 1341 4892 1641 13 6848 3380 1735 13215 14466 1251

1961 814 1095 5153 1684 16 6841 3104 2022 13112 14013 901

1962 879 1090 5223 1735 14 6684 2829 1797 13743 15045 1302

1963 852 1142 5220 1770 12 6488 2779 1902 13821 14833 1012

1964 958 1141 5121 1752 10 6689 2802 2036 13497 14464 967

1965 1057 1164 4988 1784 14 6470 2556 1537 14453 15502 1049

1966 1128 1149 4886 1812 22 6476 2289 2129 14132 14972 840

1967 1146 1166 4844 1810 13 6421 2114 1913 14597 15446 849

1968 1163 1163 4828 1817 13 6235 2007 1706 15097 16657 1560

1969 1236 1171 4760 1828 14 6293 2316 3114 13311 14257 946

1970 1342 1173 4709 1835 11 6378 3008 2556 13095 14267 1172

1971 1355 1162 4716 1807 9 6112 2326 1443 15179 16729 1550

1972 1401 1346 4705 1805 9 6112 1884 1762 15263 16773 1510

1973 1437 1376 4720 1799 8 5881 2033 2168 14859 16097 1238

1974 1549 1386 4568 1808 8 5636 1884 1461 15966 17885 1919

1975 1640 1407 4479 1823 9 5705 2030 3218 13958 15711 1753

1976 1874 1427 3132 1803 11 6647 2251 1933 15105 17163 2058

1977 1986 1454 3044 1814 13 6606 2223 1992 15060 16898 1838

1978 1967 1461 3006 1825 9 6604 2192 1991 15168 16923 1755

1979 2023 1469 2959 1833 10 6881 2118 1936 15470 17495 2025

1980 2069 1509 2931 1841 9 6406 2275 2988 14207 16371 2164

1981 2087 1507 2916 1833 24 6415 2089 2085 15267 17349 2082

1982 2077 1511 2959 1836 32 6206 2063 1969 15577 18596 3019

1983 2150 1510 2891 1835 50 6195 1920 2020 15659 18394 2735

1984 2162 1519 2879 1845 82 5740 1854 1915 16234 18877 2643

1985 2193 1500 2867 1851 29 6054 2024 2505 15215 17286 2071

1986 2227 1521 2816 1840 34 5988 2228 2016 15563 18137 2573

1987 2247 1658 2800 1824 26 5754 2237 2256 15429 17640 2211

1988 2272 1603 2868 1817 32 5985 3006 5139 11514 13308 1794

1989 2307 1656 2801 1799 26 5717 2287 1526 16123 18838 2715

1990 2324 1624 2819 1802 23 5628 2082 2340 15606 17903 2297

1991 2353 1490 2789 1912 22 5566 1927 1814 16377 19379 3002

1992 2369 1638 2754 1787 19 5567 2174 2458 15489 18093 2603


66

1993 2394 1647 2727 1770 17 5351 1864 1539 16938 20167 3228

1994 2425 1661 2684 1763 15 5282 1984 2194 16231 19254 3022

1995 2450 1667 2670 1751 17 5165 1832 1670 17021 20380 3359

1996 2458 1679 2656 1745 15 5103 1972 2035 16575 19672 3097

1997 2476 1685 2647 1735 14 5043 2020 1826 16789 20693 3903

1998 2528 1698 2621 1722 14 5017 1988 1596 17074 22325 5250

1999 2556 1705 2602 1717 14 5069 2287 2237 16073 21401 5327

2000 2580 1725 2580 1714 14 4987 2511 2637 15509 19286 3777

2001 2606 1739 2566 1707 13 4907 2444 2415 15864 19230 3365

2002 2645 1751 2520 1698 13 4730 2321 1819 16765 20798 4033

2003 2651 1764 2514 1703 12 4866 3259 6688 10807 13217 2410

2004 2660 1760 2498 1708 14 4546 2407 1275 17394 21664 4269

2005 2660 1775 2490 1708 13 4602 2162 2302 16548 21062 4513

2006 2675 1823 2439 1708 21 4590 2264 1910 16836 21699 4863

2007 2698 1835 2427 1706 20 4611 2265 1939 16764 21534 4770

2008 2713 1903 2361 1702 19 4474 2186 1752 17158 22153 4995

2009 2728 1970 2295 1699 18 4336 2108 1565 17551 22771 5220

2010 2742 1889 2379 1694 21 4233 1726 1235 18349 26002 7653

2012 2747 1884 2387 1694 21 4169 1855 1475 18034 24504 6471

(Source: Table 1 of 50 years Agricultural Statistics of Rajasthan 1956-57 to 2005-06-

May 2008; Table 13.3 of Statistical Abstract-2012 published by Directorate of Economics

and Statistics, Rajasthan; and Rajasthan Agricultural statistics at a glance for the year

2012-13-October 2014 published by Commissionerate of Agriculture, Rajasthan)

Barren, culturable waste and fallow land give smooth surface without obstruction,

thereby having the contributing effect on runoff. Area put to non-agricultural uses viz.

forest+pasture+tree crops and the net area sown increases the surface roughness, thereby

having the reducing effect on runoff. Levels of roads and highways are kept above the

natural ground level and are formed by digging soil along the roads, highways, and other

nearby borrow areas, which obstruct the natural surface runoff. Rainwater harvesting in

the residential areas, poor drainage system for storm water, and other constructional

activities increases the surface roughness, thereby area put to non-agricultural uses is

considered as having the reducing effect on runoff. Accordingly, year wise bifurcation of

the geographical area of the state has been done in two categories viz. an area having the

contributing effect on surface runoff and area having reducing effect on surface runoff.


67

Table 3.4 Trend of contributing and reducing effect of land use of Rajasthan State on

runoff (000 sqkm)

Year

Contributing effect Reducing effect

Barren Culturable

Waste Fallow Total

Non-Ag.

Uses

Forest+

pasture+

tree crops

Net area

sown Total

1 2 3 4 5 6 7 8 9

1957 49.12 73.26 55.59 177.9

7

10.90 28.64 124.25 163.79

1958 49.11 73.13 60.78 183.0

2

11.25 25.77 121.03 158.05

1959 48.96 70.63 57.05 176.6

4

11.68 26.95 125.88 164.51

1960 48.92 68.48 51.15 168.5

5

13.41 24.31 132.15 169.87

1961 51.53 68.41 51.26 171.2

0

10.95 25.14 131.12 167.21

1962 52.23 66.84 46.26 165.3

3

10.90 26.28 137.43 174.61

1963 52.20 64.88 46.81 163.8

9

11.42 26.34 138.21 175.97

1964 51.21 66.89 48.38 166.4

8

11.41 27.20 134.97 173.58

1965 49.88 64.70 40.93 155.5

1

11.64 28.55 144.53 184.72

1966 48.86 64.76 44.18 157.8

0

11.49 29.62 141.32 182.43

1967 48.44 64.21 40.27 152.9

2

11.66 29.69 145.97 187.32

1968 48.28 62.35 37.13 147.7

6

11.63 29.93 150.97 192.53

1969 47.60 62.93 54.3 164.8

3

11.71 30.78 133.11 175.60

1970 47.09 63.78 55.64 166.5

1

11.73 31.88 130.95 174.56

1971 47.16 61.12 37.69 145.9

7

11.62 31.71 151.79 195.12

1972 47.05 61.12 36.46 144.6

3

13.46 32.15 152.63 198.24

1973 47.20 58.81 42.01 148.0

2

13.76 32.44 148.59 194.79

1974 45.68 56.36 33.45 135.4

9

13.86 33.65 159.66 207.17

1975 44.79 57.05 52.48 154.3

2

14.07 34.72 139.58 188.37

1976 31.32 66.47 41.84 139.6

3

14.27 36.88 151.05 202.20

1977 30.44 66.06 42.15 138.6

5

14.54 38.13 150.60 203.27

1978 30.06 66.04 41.83 137.9

3

14.61 38.01 151.68 204.30

1979 29.59 68.81 40.54 138.9

4

14.69 38.66 154.70 208.05

1980 29.31 64.06 52.63 146.0

0

15.09 39.19 142.07 196.35

1981 29.16 64.15 41.74 135.0

5

15.07 39.44 152.67 207.18

1982 29.59 62.06 40.32 131.9

7

15.11 39.45 155.77 210.33

1983 28.91 61.95 39.4 130.2

6

15.10 40.35 156.59 212.04

1984 28.79 57.40 37.69 123.8

8

15.19 40.89 162.34 218.42

1985 28.67 60.54 45.29 134.5

0

15.00 40.73 152.15 207.88

1986 28.16 59.88 42.44 130.4

8

15.21 41.01 155.63 211.85

1987 28.00 57.54 44.93 130.4

7

16.58 40.97 154.29 211.84

1988 28.68 59.85 81.45 169.9

8

16.03 41.21 115.14 172.38

1989 28.01 57.17 38.13 123.3

1

16.56 41.32 161.23 219.11

1990 28.19 56.28 44.22 128.6

9

16.24 41.49 156.06 213.79

1991 27.89 55.66 37.41 120.9

6

14.90 42.87 163.77 221.55

1992 27.54 55.67 46.32 129.5

3

16.38 41.75 154.89 213.02

1993 27.27 53.51 34.03 114.8

1

16.47 41.81 169.38 227.66

1994 26.84 52.82 41.78 121.4

4

16.61 42.03 162.31 220.95

1995 26.70 51.65 35.02 113.3

7

16.67 42.18 170.21 229.06

1996 26.56 51.03 40.07 117.6

6

16.79 42.18 165.75 224.72

1997 26.47 50.43 38.46 115.3

6

16.85 42.25 167.89 226.99

1998 26.21 50.17 35.84 112.2

2

16.98 42.64 170.74 230.36

1999 26.02 50.69 45.24 121.9

5

17.05 42.87 160.73 220.65


68

2000 25.80 49.87 51.48 127.1

5

17.25 43.08 155.09 215.42

2001 25.66 49.07 48.59 123.3

2

17.39 43.26 158.64 219.29

2002 25.20 47.30 41.4 113.9

0

17.51 43.56 167.65 228.72

2003 25.14 48.66 99.47 173.2

7

17.64 43.66 108.07 169.37

2004 24.98 45.46 36.82 107.2

6

17.60 43.82 173.94 235.36

2005 24.90 46.02 44.64 115.5

6

17.75 43.81 165.48 227.04

2006 24.39 45.90 41.736

6

112.0

3

18.23 44.04 168.36 230.63

2007 24.27 46.11 42.044

37

112.4

3

18.35 44.23 167.64 230.22

2008 23.61 44.74 39.387

475

107.7

3

19.03 44.34 171.58 234.94

2009 22.95 43.36 36.730

58

103.0

4

19.70 44.44 175.51 239.66

2010 23.79 42.33 29.61 95.73 18.89 44.57 183.49 246.95

2012 23.87 41.69 33.3 98.86 18.84 44.62 180.34 243.80

Due to regular increase in the population of the state, fields are being regularly

subdivided resulting in regularly reducing the value of average field size. Accordingly,

changes in number and area of operational holdings and the average size of the field in

the state is shown in Table 3.5.

Table 3.5 Variation of operational land holdings in the state

Year

Aver

age

Marginal

<1.0 ha

Small

1 to 2 ha

Semi-medium

2 to 4 ha

Medium 4

to 10 ha

Large

> 10.0 ha Total

Size

(ha)

Nu

mb

ers

(mil

lio

ns)

Area

(mh

)

Nu

mb

ers

(mil

lio

ns)

Area

(mh

)

Nu

mb

ers

(mil

lio

ns)

Area

(mh

)

Nu

mb

ers

(mil

lio

ns)

Area

(mh

)

Nu

mb

ers

(mil

lio

ns)

Area

(mh

)

Nu

mb

ers

(mil

lio

ns)

Area

(mh

)

1970 5.46 0.94 0.46 0.69 1.00 0.77 2.24 0.80 5.02 0.52 11.62 3.73 20.34

1975 4.65 1.32 0.58 0.80 1.15 0.87 2.48 0.87 5.49 0.51 10.59 4.37 20.30

1980 4.44 1.32 0.63 0.88 1.27 0.92 2.62 0.89 5.52 0.41 9.88 4.49 19.93

1985 4.34 1.36 0.64 0.92 1.33 0.98 2.79 0.99 6.15 0.50 9.68 4.74 20.59

1990 4.11 1.52 0.73 1.02 1.47 1.06 3.02 1.02 6.33 0.49 9.42 5.11 20.97

1995 3.96 1.61 0.78 1.09 1.57 1.12 3.19 1.06 6.62 0.49 9.10 5.36 21.25

2000 3.65 1.85 0.89 1.21 1.74 1.20 3.42 1.10 6.81 0.46 8.39 5.82 21.25

2005 3.38 2.07 1.02 1.32 1.90 1.26 3.57 1.10 6.80 0.43 7.66 6.19 20.94

For Ramgarh dam, the land use of the catchment area is shown in Table 3.6. It

consists of 17.25% barren, culturable waste and fallow land having contributing effect to

runoff, and 82.75% agriculture, forest, residential areas, roads, mining, etc. having

reducing effect to the runoff in the year 2010.


69

Table 3.6 Land utilization of the Ramgarh dam catchment (%)

Year


Barren Culturable

waste Fallow Total

Non-Ag.

uses

Forest

+pasture

+tree crops

Net area

sown Total

1978 14.09 2.86 12.65 29.60 4.09 18.66 47.65 70.40

1980 13.82 2.83 11.55 28.20 4.01 19.79 48.00 71.80

1983 16.45 3.77 10.16 30.37 4.01 21.16 44.46 69.63

1985 15.38 4.09 7.56 27.03 4.19 21.42 47.35 72.97

2003 8.64 2.64 14.57 25.84 4.85 27.83 41.48 74.16

2005 4.93 3.30 13.98 22.20 7.11 14.43 56.25 77.80

2010 5.38 3.44 8.44 17.25 7.35 14.22 61.17 82.75

(Source: Table 4.5 of Statistical outlines of Jaipur District of various years and Statistical

Abstract published by Directorate of Economics and Statistics, Rajasthan)

For Bisalpur dam, the land use of the catchment area is shown in Table 3.7,

consisting of 30.97% barren, culturable waste and fallow land having contributing effect

to runoff, and 69.03% agriculture, forest, residential areas, roads, mining, etc. having the

reducing effect on the runoff in the year 2012.

Table 3.7 Land Utilization of the Bisalpur Dam Catchment (%)

Year


Barren Culturable

Waste Fallow Total

Non-Ag.

Uses

Forest+

pasture+

tree crops

Net area

sown Total

1978 16.03 21.41 8.15 45.59 5.43 17.91 31.07 54.41

1980 15.94 21.52 8.44 45.90 5.53 18.09 30.48 54.10

1985 14.49 19.66 7.66 41.80 5.84 18.50 33.85 58.20

1992 14.51 16.86 8.07 39.45 4.99 19.27 36.30 60.55

1995 12.88 16.14 7.93 36.94 5.28 20.21 37.57 63.06

2000 12.06 15.61 10.76 38.43 5.60 21.06 34.91 61.57

2003 11.87 15.04 12.08 38.99 5.71 21.30 34.01 61.01

2006 11.37 12.59 6.63 30.58 5.76 21.98 41.68 69.42

2008 11.22 12.64 7.44 31.30 5.78 22.02 40.89 68.70

2010 12.10 12.68 9.12 33.90 6.13 20.71 39.26 66.10

2012 12.15 11.50 7.32 30.97 6.08 20.78 42.17 69.03

(Source: Table 4.5 of the statistical outline of Ajmer, Bhilwara, Chittorgarh and Jaipur

Districts; Table 13.3 of Statistical Abstract-2001, 2003, 2010, and 2012 published by

Directorate of Economics and Statistics, Rajasthan; and Rajasthan Agricultural statistics

at a glance for the year 2012-13-October 2014 published by Commissionerate of

Agriculture, Rajasthan)


70


Pre and Post monsoon groundwater levels below ground levels of the year 1984 to 2013

(30 years) of 9628 gauge sites of the state have been obtained from Senior Hydro-

Geologist (D.S.P.C.), Ground Water Department, Jodhpur vide his letter no. 52 dated

23.04.2015, under Right to Information Act-2005. Data was also obtained from Senior

Hydro-Geologist (Survey and Investigation), Ground Water Department, Jaipur vide his

letter no. F./SHG/Estt./GWD/Jaipur/2015/2846 dated 17.03.2015. It is not possible to

attach the data of 9628 gauge sites of the last 30 years. However, year wise average pre

and post monsoon water levels of the state are shown in Table 3.8, and average pre and

post-monsoon groundwater levels below the ground level of districts and basins are

shown in Appendix 2(b).

Table 3.8 Year wise average depth to water table below ground level of the state

S.N. Year Pre-monsoon Avg.

GWL BGL (m)

Post-monsoon Avg.

GWL BGL (m)

Difference of Avg.

GWL BGL (m)

1 1984 18.0 17.3 0.68

2 1985 19.1 17.5 1.59

3 1986 19.6 19.1 0.54

4 1987 20.1 19.9 0.21

5 1988 21.9 19.4 2.42

6 1989 21.0 19.1 1.90

7 1990 21.6 18.5 3.07

8 1991 20.3 18.6 1.67

9 1992 20.9 17.7 3.20

10 1993 19.9 18.4 1.46

11 1994 20.8 17.4 3.39

12 1995 19.9 17.5 2.44

13 1996 19.6 19.5 0.05

14 1997 18.8 16.5 2.27

15 1998 18.6 17.3 1.33

16 1999 19.7 18.4 1.25

17 2000 20.0 19.1 0.89

18 2001 21.3 18.3 3.00

19 2002 21.2 21.3 -0.03

20 2003 23.5 19.3 4.18

21 2004 22.5 19.8 2.69

22 2005 22.1 19.5 2.61

23 2006 22.9 19.2 3.61

24 2007 21.9 20.2 1.71

25 2008 22.9 21.3 1.64


71

26 2009 23.4 21.9 1.51

27 2010 24.6 20.1 4.48

28 2011 23.0 18.8 4.20

29 2012 22.1 18.6 3.49

30 2013 21.8 20.2 1.64

Min. 18.0 16.5 -0.03

Max. 24.6 21.9 4.50

The groundwater table of the state is showing an improvement after the year 2010.

During the year 2002, post monsoon water table decreased due to scanty rainfall leading

to more withdrawal than recharge even during the monsoon period.

3.2.6 Population Data

Population data of the state have been obtained from Department of Economics and

Statistics. The decadal population of the country, state, and Jaipur district has been shown

in Table 3.9.

Table 3.9 Population data

Year

India Rajasthan State Jaipur District

Millions

%

Decadal

increase

Millions % Share in

country

%

Decadal

increase

Millions % Share

in state

%

Decadal

increase

1901 238.4

10.3 4.3

1911 252.1 5.7 11.0 4.4 6.8

1921 251.3 -0.3 10.3 4.1 -6.4

1931 279.0 11.0 11.7 4.2 13.6

1941 318.7 14.2 13.9 4.4 18.8

1951 361.1 13.3 16.0 4.4 15.1

1961 439.2 21.6 20.2 4.6 26.3

1971 548.6 24.9 25.8 4.7 27.7 2.5 9.6

1981 683.3 24.6 34.3 5.0 32.9 3.4 10.0 37.9

1991 846.4 23.9 44.0 5.2 28.3 4.7 10.7 38.0

2001 1028.7 21.5 56.5 5.5 28.4 5.3 9.3 11.2

2011 1210.2 17.6 68.6 5.7 21.4 6.6 9.7 26.3

2021 1394.0 15.2 82.6 6.2 20.4 8.0 9.7 21.1

(Source: Population facts and figures-Rajasthan June 2008; Table 1.32 of Some facts

about Rajasthan-September 2012; Statistical Abstracts; and Basic Statistics of Rajasthan

published by Directorate of Economics and Statistics, Rajasthan)


72

3.3 Methodology

To test the temporal variation of inflow to the dams of Rajasthan state, t-test and Chi-

Square test has been applied. Detailed computations have been done for Exceedance

Probability, which is mentioned as Dependability as per prevailing nomenclature in

Government of Rajasthan. This can also be termed as reliability. Performance measures

and performance statistics have been applied. Time series analysis and sequential cluster

analysis has been done to determine the critical year, which divides two consecutive

nonoverlapping epochs- pre-disturbance and post-disturbance (Wang et al. 2001).

Regression analysis and Cosine Amplitude Method (CAM) of sensitivity analysis have

been used to find out the most effective input parameters on the output parameters

(Sayadi et al. 2013). Trend analysis and regression analysis can be used to find the

various correlations (Kumar et al. 2014, Parmar et al. 2014). These techniques have been

used in this study.

3.3.1 Sampling

Cluster sampling method has been used to take the sample for this study. Further special

analyses have been done for Ramgarh and Bisalpur dams. The major and medium dams

are situated in eleven river basins. Therefore, the test has been applied for these eleven

river basins only out of total fifteen river basins in the state. Four river basins do not have

any major or medium dam within their catchment area. By the storage capacity of the

basins, weights have been considered to arrive at weighted dependability. The

Government of Rajasthan (Water Resources Department 2002, State Water Resources

Planning Department 2011) has declared expected dependability as 50% for minor dams

and 75% for medium and major dams. Observed dependabilities have been computed for

each of 115 selected dams. Furthermore, area-sampling method has been used to select 33

dams of the Tonk District to conduct performance analysis.

3.3.2 Inflow Trend Analysis

Rainfall and water inflow series are plotted to see the temporal variation of rainfall and

inflow to the dams or reservoirs (Yildirim et al. 2011). The results of these series reveal

the trend of inflow and precipitation based on real time data. Dependability is the

exceedance probability, which ensures the certain percentage of time when inflow will


73

exceed the desired quantum of water (Liu et al. 2005, Mantel et al. 2010, Chang et al.

2011; Kim et al. 2012). It can be computed as shown below:

1001

m

nDn

(3.1)

Where Dn= dependability, n= number of years when inflow exceeds the design

capacity of the dam, m= total number of years of observation. Daily precipitation data are

analyzed to obtain average annual rainfall, average monthly rainfall, and average daily

rainfall in rainy days etc. (Erturk et al. 2014)

3.3.3 Formulation and Testing of Hypothesis

Change in water inflow can be tested by the formation of the null hypothesis and testing

the same. In this study, null hypotheses claiming no change in expected dependability are

formed and tested with the Chi-Square test.

iii

n

i

EEO /2

1

2

(3.2)

Where 2 = computed Chi-Square value, Oi= observed weighted dependability,

Ei= expected weighted dependability and n= total number of years i.e. sample size. To

test the hypothesis computed Chi-Square value is compared with the tabulated Chi-

Square value. After testing the hypothesis, it can be inferred whether there is a change in

expected dependability and inflow to the dams or not.

In case n<30, the t-test is applied to check whether the inflow is equal to certain

acceptable value. The t-value is computed as

xt ; Where

n

S ; where

1

)(1

2

n

xx

S

n

i

(3.3)

Where t = computed t-value, S.E.= standard error of population, S= standard

error of sample, and n= total number of years i.e. sample size, x = sample mean value.

This computed t-value is compared with the critical t-value obtained from standard t-

distribution table. In case n>30, Z-test has been applied.


74

3.3.4 Performance Measures and Performance Statistics

Hashimoto et al. (1982) and Loucks and van Beek (2005) as cited by Solis et al. (2011)

used performance criteria to characterize the hydrologic scenario viz. reliability,

vulnerability, and resilience. Reliability is the frequency with which a water demand is

satisfied over the simulation period, which can be described as below:

(3.4)

Where, m= simulation period and n= frequency with which a water demand is satisfied.

Vulnerability is the magnitude of water delivery deficits over the period of Simulation as

a percentage of the demand (average deficit over the period of simulation as percent of

total demand), which can be described as below:

100Demand

Deficit Average(%) ityVulnerabil

(3.5)

Resilience is the probability that a deficit period will be followed by a successful period

during the simulation period, which can be described as below:

100'

'(%)

m

nResilience

(3.6)

Where n’= number of years when deficit period is followed by a successful period and

m’= total number of deficit years.

Sustainability index can be used to summarize the three performance criteria discussed

above. The geometric average of reliability, resilience, and vulnerability is termed as

sustainability index, which can be described as below:

31

Vul1ResRelSust (3.7)

Maximum deficit (%) is also calculated for the simulation period and the year

corresponding to the maximum deficit is determined. The performance indices were also

used by Hashimoto et al. (1982) and Loucks (1997) as cited by Carrasco et al. (2012).

Theoretical inflow has been computed by employing Thiessen polygon and Strange’s

Table techniques (Subramanya 2013, Reddy n.d., Sharma and Sharma, Punmia,

Satyanarayana Murty 2006, Water Resources Department 2002, 2011). Comparison of

theoretical and observed inflow is based on the Nash-Sutcliffe Efficiency- Ens (Nash and

100(%) m

nyReliabilit


75

Sutcliffe 1970; Hayashi et al. 2008; Sang et al. 2010; Pundik et al. 2014). Some other

statistical parameters are also employed for judging the performance of simulation

method.

2

1

N

i

ii SOSSE

(3.8)

2

1

1

N

i

ii SON

RMSE

(3.9)

1001

(%)1

N

i i

ii

O

SO

NMAPE

(3.10)

N

i i

ii

O

SO

NMAE

1

1

(3.11)

N

i

N

i

ii

N

i

ii

SSOO

SSOO

R

1 1

22

1

(3.12)

N

i

i

N

i

ii

OO

SO

NSE

1

2

1

2

1

(3.13)

N

i

i OON

RMSERSR

1

21

(3.14)

100

/1

/1

(%)

1

1

N

i

i

N

i

ii

ON

OSN

NMBE

(3.15)

Where Oi and Si denote the observed values and simulated values; O and S

represent the mean observed and mean simulated values respectively. N= total number of


76

data; SSE= sum of square error; RMSE= root mean square error; MAPE= mean absolute

percentage error; MAE= mean absolute error; R= Karl Pearson’s correlation coefficient;

NSE= Nash-Sutcliffe Efficiency; RSR= RMSE to observation’s standard deviation ratio;

NMBE= normalized mean bias error.

3.3.5 Determination of Critical Year

Time Series Analysis has been carried out to find out the possible critical years (Bolgov

et al. 2012, Dourte et al. 2012). Further, the critical year has been determined by

Sequential Cluster Analysis. The critical year divides two consecutive nonoverlapping

epochs- pre-disturbance and post-disturbance. The minimum sum of squared deviations

for the hydrological series before and after the possible critical years gives the unique

critical year (Wang et al. 2001).

2

1

)(

n

i

nin OOV ;

N

ni

nNinN OOV1

2)( (3.16)

Where Oi = observation of ith year; nO = mean of first ‘n’ observations; nNO = mean of

(n+1) to Nth observation; Vn= squared deviation for the hydrological series before the

possible critical year; VN-n= squared deviation for the hydrological series after the

possible critical year.

3.3.6 Various Factors, their Trend Analysis, and Correlation Matrix

Graph of various factors has been plotted against the time scale. The trend analysis gives

us an idea about the direction and rate of change of various factors, which may influence

the inflow to the dams (Yates et al. 2009, Kravtsova et al. 2009, Khaustov and Redina

2010). A correlation matrix (N x N) has been prepared to identify the mutual correlation

of each of the parameters and correlation of each parameter with the inflow (output).

3.3.7 Regression Analysis and Determination of Principal Components

Various trends can be plotted for rainfall pattern, climatic factors, GWL BGL, population

density etc. with actual inflow. LULC trends and correlations can also be plotted to assess

the effect of anthropogenic changes over catchment areas (Liu et al. 2005, Danil’yan

2005; Danil’Yan et al. 2006, Draper and Kundele 2007, Shah and Kuma 2008, Mantel et

al. 2010, Shiklomanov et al. 2011, Lemeshko 2011, Yildirim et al. 2011, Chang et al.


77

2011, Wang et al. 2011, Handhong et al. 2012, Kumar et al. 2014). The correlation

coefficient (r) was used to measure the degree of fitness of the relationship between

model parameters and basin properties to find out which basin property is most closely

related to the model parameter (Piman and Babel 2012, Parmar et al. 2014).

Regression analysis is performed between independent (x) and dependent variables (y).

The Karl Pearson’s correlation coefficient (r) is computed as

22

1

yyxx

yyxx

r

ii

n

i

ii

(3.17)

The strength of relationship is tested by applying t-test with the help of computed r-value.

The Hypothesis is formed as

H0: r=0

H1: r≠ 0 for a two-tailed test or it may be framed for a one-tailed test.

2

1 2

n

r

rtr

(3.18)

Where tr= computed value of‘t’; r = correlation coefficient as computed in equation 3.17;

n= number of observations. Computed value of tr is compared with the tabulated value of

t (tc) to test the hypothesis.

3.3.8 Cosine Amplitude Method of Sensitivity Analysis and Determination of

Principal Components

Sensitivity analysis using Cosine Amplitude Method (CAM) is used for finding the

relative importance of the factors responsible for the observed output. This technique was

employed to find out the most effective input parameters on the output parameters.

(Sayadi et al. 2013). Firstly, data are normalized by employing the formula

minmax

min

xx

xxx i

n

(3.19)

Where xn= normalized value of ith

observation; xi= value of ith observation; xmin=

minimum value in all observations; xmax= maximum value in all observations.

The strength of this relation (Ri) is calculated by


78

n

i

ii

n

i

ii

i

yx

yx

R

1

22

1 (3.20)

Where xi= value of normalized ith

observation of the input parameter; yi= value of

normalized ith observation of the output parameter.

Many researchers used various methods of sensitivity analysis for varied purposes,

noticeable of them are Cosine Amplitude Method (CAM), Principal Component Analysis,

MLR, Profile Method, Partial Derivative (PaD) Method, Perturb Method, Weights and

Improved Weights Method etc (Jain et al. 2008, Wang et al. 2008, Sang et al. 2010,

Karamouz et al. 2010, Carrasco et al. 2012)

3.3.9 Multiple Linear Regressions (MLR) for generation of Inflow Equation

Multiple linear regression analysis has been done using the Excel software employing

add-ins of the analysis tool pack, which provides data analysis tools for statistical and

engineering analysis. The results of MLR have been correlated with observed values to

assess the significance of (R) correlation coefficient; t-test has been applied. Runoff

equation using multiple linear regressions was used by Jain et al. (2008) to compute

runoff using rainfall and ground water table data.

6655443322110 ****** xxxxxxInflow (3.21)

3.3.10 Regression Analysis and Determination of Factors Responsible for Principal

Components

Regression analyses have been conducted to find out the factors, which may be

considered responsible for principal components.


79

3.4 Methodology Flow Chart

Fig. 3.3 Methodology Flow Chart

Summary of Methodology by Flow Chart

Need of the study, Objective of the Study,

Generation of Hypothesis

Data Collection and Field Visit

Data from

WRD

Data from

GWD

Data from

Statistical

Department

Data from

online

sources

Testing of Hypothesis, Trend Analysis, t-test, Z-test, and Chi-square test,

Dependability Analysis, Performance measure and Performance Statistics

Determination of Critical Year:

Time Series Analysis and Sequential Cluster Analysis

Determination of Principal Components: Correlation & Regression Analysis, t-test,

Cosine Amplitude Method (CAM) of Sensitivity Analysis

Determination of Factors responsible:

Correlation & Regression Analysis, t-test

Generation of Inflow Equation using Multiple Linear

Regression Technique and Finding Goodness of Fit


80

3.5 Underlying Assumptions

Following assumptions are made for the present study, which is described below:

Regular data of reasonable nos. of gauge discharge sites are not recorded by Water

Resources Department of the state. Therefore, theoretical inflows have been computed

based on Strange’s table, which assumes the whole catchment of the dam as uniform

and accordingly some constant is assumed.

Monsoon rainfall during 15 June to 30 September is considered as annual rainfall, as

approx. 90% of the annual rainfall occurs during monsoon season.

Inflow data recorded during monsoon period is assumed as annual inflow.

Some data, which are not available, have been interpolated.

There are thousands of water bodies in the state. To study the temporal variation of

inflow to the dams of the Rajasthan state, only medium and major dams have been

considered. These dams have large amount of storage capacity. Important drinking

water supply schemes in the state are mainly dependent on these dams. Therefore, it is

assumed that medium and major dams represent the whole state.

Mean of climatic data of all stations within the catchment has been used, assuming the

uniform climatic conditions.

Even though residential areas, roads, pavements are smooth surface, but for surface

runoff it is assumed rough/obstructed catchment due to poor drainage conditions and

resulting water accumulation at various points leading to reducing effect on surface

runoff.

It is assumed that small land holdings increase the roughness the catchment due to

increased network of boundaries.

Forest, pasture, and tree crops increase the soil moisture and infiltration. It is assumed

that these land uses also increase the surface roughness, which leads to less surface

runoff.

Agricultural activities loose the upper crust of the catchment. Therefore, it is assumed

that these land use have reducing effect on the surface runoff.

Barren, culturable waste and fallow land provide smooth surface. Therefore, it is

assumed that these land uses have contributing effect on surface runoff.


81

4 TEMPORAL VARIATION OF INFLOW TO THE DAMS

OF RAJASTHAN STATE

4.1 Formulation and Testing of Hypothesis

4.2

Performance Study and Performance Statistics

4.3 Determination of Critical Year


Series Analysis


Cluster Analysis

lysis

4.4 Various Parameters and their Trends

4.4.1 Rainfall and other Climatic Parameters

4.4.2 Land Use Land Cover Changes


4.4.4 Population Growth


of Changes

4.4.6 Correlation Matrix

4.5 Determination of Principal Components

4.5.1 Regression Analysis

4.5.2 Cosine Amplitude Method

4.6 MLR for Generation of Inflow Equation




82

CHAPTER-4

TEMPORAL VARIATION OF INFLOW TO THE DAMS OF

RAJASTHAN STATE

Rajasthan is a semi-arid state where, only 25% of time three months in a year, rainfall

takes place which is also erratic and spatial in nature. There are average 35 rainy days in a

year. Therefore, water availability in surface water reservoirs plays an important role in

state water resources planning and management. Conjunctive use of surface and

groundwater becomes essential when critical (very small) total runoff values are

maintained over several successive years, which is also termed as critical N-year groups

(Bolgov et al. 2012). This type of situation arises in many dams of the state, which is also

reflected in the inflow data of Ramgarh and Bisalpur dams.

Temporal variation of inflow to the dams of Rajasthan State has been shown in

Fig. 4.1. Year wise inflow data have been shown in Table 4.1. It is evident that the water

inflow trend to the dams is declining. Inflow to the dams was around 74% during 1990-

1997 which has reduced to around 60% during 1998-2013. The average inflow by last 24

years data (1990-2013) is 65%. Fig. 4.1 shows that there was a slight improvement in

inflow during 2010-2013. The percent inflow was nearly constant around 62-78% during

first time segment (1990-1997), it was very less during second time segment (1998-2005),

while it is improving during third time segment (2006-2013) primarily because of more

than average rainfall. It can be observed from Fig. 4.1(b) that inflow during third-time

segment is near to second time segment during low annual rainfall, and it is touching to

the first-time segment during high annual rainfall.

Total storage capacity of the state increases with the construction of new storage

reservoirs in the state. Total utilizable water availability in the state is 16050 MCM

(Table 1.1 Column 3). The storage capacity of new dams is designed as per the

hydrological computations, which is considered as expected inflow to the dam, without

intercepting the inflow to the existing dams (Table 4.1 Column 2). Sustainability of the

projects are considered when they perform as per design dependability. Total inflow to

the dams of the state in the year 1992 was 6109 MCM out of total expected inflow of


83

9233 MCM, yielding 66.2% inflow, while it was 6433 MCM against total expected

inflow of 12486 MCM in the year 2010, yielding only 51.5 % inflow. This infers that

there was lesser inflow to the dams of the state in the year 2010 than the year 1992 even

after getting a more total inflow of water. Therefore, percentage inflow is considered for

analysis of inflow to the dams of Rajasthan state.

Inflow (%) with respect to dependability is plotted as shown in Fig 4.2. 1998-2003

and 2008-2010 may be considered as the critical N-years groups when inflow was below

60% of the capacity. The graph shows that there was only 30.9% inflow (which is 100%

reliable) during 2002 (which was the year of the maximum deficit). Dependability,

reliability and exceedance probability are considered similar in a broad sense. 100%

reliability is considered for drinking water projects (IS 5477 Part-3:2002).

Table 4.1 Year wise inflow (%) to the dams of Rajasthan state

S.N. Year Total gross

storage (MCM)

Total inflow

(MCM)

Inflow

(%)

Inflow in

descending order

Dn

1 1990 9233 6556 71.0 85.8 4.0

2 1991 9233 7251 78.5 83.6 8.0

3 1992 9233 6109 66.2 78.5 12.0

4 1993 9233 5780 62.6 77.6 16.0

5 1994 9233 7919 85.8 76.78 20.0

6 1995 9233 7165 77.6 76.71 24.0

7 1996 9233 7082 76.7 76.7 28.0

8 1997 9233 6564 71.1 73.35 32.0

9 1998 9321 5386 57.8 71.1 36.0

10 1999 11121 4847 43.6 71.0 40.0

11 2000 11121 4674 42.0 71.0 44.0

12 2001 11121 6495 58.4 71.0 48.0

13 2002 11121 3433 30.9 69.7 52.0

14 2003 11121 6459 58.1 66.2 56.0

15 2004 11121 7897 71.0 62.6 60.0

16 2005 11124 7898 71.0 58.4 64.0

17 2006 11124 9298 83.6 58.1 68.0

18 2007 11124 7756 69.7 57.8 72.0

19 2008 11124 6252 56.2 56.2 76.0

20 2009 11475 4735 41.3 51.5 80.0

21 2010 12486 6433 51.5 43.6 84.0

22 2011 12486 9158 73.3 42.0 88.0

23 2012 12535 9624 76.8 41.3 92.0

24 2013 12535 9615 76.7 30.9 96.0

Average= 65.0


84

(a) Water inflow trend

(b) Water inflow trends for different time segments

Fig. 4.1 Temporal variations of water inflow to the dams of Rajasthan state

3000

5000

7000

9000

11000

13000

15000

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

1990 1995 2000 2005 2010 2015

Gross

in

flow

(M

CM

)

Percen

tage I

nfl

ow

(%

)

Temporal Variation of Inflow 1990-2013

Percentage inflow Gross inflow

Linear (Percentage inflow) Linear (Gross inflow )

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

200 300 400 500 600 700 800

Infl

ow

(%

)

Rainfall (mm)

Inflow (%) trend for differenct time segment

1990-1997 1998-2005 2005-2013

Linear (1990-1997) Linear (1998-2005) Linear (2005-2013)


85

Fig. 4.2 Variations of inflow with dependability in the dams of Rajasthan state

Fig. 4.3 Number of dams spillover in the state

The number of dams spillover reduced after the year 2001 as shown in Fig. 4.3, which

shows a reduced inflow to the dams of Rajasthan State.

4.1 Formulation and Testing of hypothesis

(1) T-test: T-test can be applied to check the sample mean for size of the sample n<30. In

this case, we want to check the average inflow and sample size is n=24 (<30), therefore t-

test has been applied.

Ho (Null Hypothesis): 75

H1 (Alternate Hypothesis): 75

65x ; S= 14.58; SE= 2.98; t= 3.36

0.0

20.0

40.0

60.0

80.0

100.0

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0

Infl

ow

(%

)

Dependability (%)

Dependability-Inflow (%) Curve

0

200

400

600

800

1000

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

2010

20

12

Nos.

of

da

ms

Number of the dams spill over

Number Poly. (Number)


86

tc for 23 degree of freedom (n-1) for left tail test at 5% significance level= 1.714

t>tc; therefore null hypothesis is rejected and alternate hypothesis is accepted, i.e. dams

are getting less than the desired 75% inflow of water.

(2) Chi-square-test: Chi-square test can be applied to test, whether there is any effect of

any change on expected result. Therefore, Chi-square test has been applied to test whether

there is any effect of time series (A=10 year/B=20 year) on the dependability of dams of

the state or not.

Table 4.2 Testing of hypothesis (Basin wise): Chi-square test

S.N. Name of Basin

Capacity of

the Basin

(MCM)

Ei % Oi % Oi % Chi2 Chi2

(A) (B) (A) (B)

1. Banas 2243 11.94 6.25 4.16 2.71 5.08

2. Shekhawati 9 0.05 0.00 0.00 0.04 0.05

3. Ruparail 27 0.14 0.01 0.00 0.12 0.14

4. Banganga 148 0.79 0.12 0.00 0.57 0.79

5. Gambhir 147 0.78 0.21 0.05 0.42 0.69

6. Parbati 16 0.08 0.02 0.00 0.05 0.08

7. Sabi 24 0.13 0.00 0.00 0.13 0.13

8. Chambal 3414 18.17 10.91 9.77 2.91 3.89

9. Mahi 2791 14.86 14.42 9.30 0.01 2.08

10. Luni 533 2.84 1.27 0.67 0.86 1.65

11. West Banas 39 0.21 0.14 0.08 0.02 0.08

Total 9391 50.00 33.36 24.02 7.84 14.66

Chi-square value for 11 numbers of basins, d1=10 (11-1); d2= (2-1) and

d=d1xd2=10 degree of freedom, at the 5% level of significance is 18.307. The computed

value is 22.5 (7.84+14.66), which is more than the critical value, this infers that there is a

change in the expected dependability with time series. Observed dependability of the

state surface water resources has reduced to 33.36% for 20 years data series and

24.02% for 10 years data series against expected dependability 50%. This shows a

sign of temporal variation of inflow to the dams of Rajasthan State.

4.2 Performance Study and Performance Statistics

On the basis of year wise inflow data (Table 4.1) hydrological performance of the dams is

measured as below. The historical minimum value of the inflow was observed in the year

2002.

Reliability: 29.17%


87

Resilience: 23.53%

Vulnerability: 15.52%

Maximum deficit: 44.13% (2002)

Sustainability: 38.70%

32 dams, comprising minor, medium and major dams, of Tonk District have been

selected through area sampling method. Actual inflow (at 50% dependability) as

percentage of design capacity is categorized on interval scale and health status of the dam

has been assessed (Table 4.3). Only 17 dams are performing safe; 6 dams are put in

critical scale; and 9 dams are under hyper-critical scale. In brief, it can be said that 17

dams (around 50% dams) are getting water more than 75% of their design capacity in

Tonk district. Theoretical values have been computed and compared with observed

values. Performance statistics of the computed values shows that the theoretical values

are not in correlation with the actual observed values. Therefore, it is inferred that the

Strange’s table method of computation of theoretical yield should not be used for inflow

assessment of the dams (Table 4.4).


88

Table 4.3 Performance study of the dams of Tonk district

S.

N. Name of Dam

Type of

Dam

Design Parameters Present Theoretical Yield based on

Rainfall(MCM) data of last 30 years

Yield based on Actual

Storage(MCM)

Mean

Rainfall

(based

on last

30

years)

Dn for

design

capacity

Actual yield

( 50% dep.)

in % of

design

capacity col.

10/4

Health Status

of Dam Safe/

Semi

Critical/Critic

al/ Hyper

Critical

Capacity

(MCM)

Mean

Rainfall

(mm)

50%

Dep.

75%

Dep.

90%

Dep. Mean

50%

Dep.

75%

Dep.

90%

Dep

.

Mean

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

1 Bisalpur # Major 938.83 588 607.0 338.0 243.3 708.6 726.1 174.6 106 977.6 520 40.91 77.34% Safe

2 Galwa Major 48.74 724 20.36 16.00 4.37 25.54 32.85 16.31 10.5 30.73 507 25.00 67.4% Critical

3 Tordi Sagar Major 47.14 686 15.17 4.53 2.73 45.98 10.11 2.85 1.15 17.32 403 16.67 21.45% Hyper Critical

4 Mashi Medium 48.15 483 59.80 30.56 7.96 91.01 22.59 13.42 0.00 26.81 461 29.17 46.91% Hyper Critical

5 Chandsen Medium 14.70 569 0.96 0.45 0.18 2.22 3.61 1.73 0.42 5.46 371 12.50 24.57% Hyper Critical

6 Moti Sagar Medium 12.89 635 4.87 1.85 0.95 6.15 11.03 6.11 3.20 9.34 453 41.67 85.60% Safe

7 Dakhiya Minor 8.58 584 1.83 0.69 0.36 2.31 3.51 0.70 0.00 3.91 453 17.65 40.92% Hyper Critical

8

Kirawal

Sagar Minor 6.51 569 1.96 0.92 0.37 4.56 2.11 0.84 0.51 2.79 371 16.67 32.43% Hyper Critical

9 Shahodra Minor 6.51 569 8.07 3.29 0.70 8.98 5.41 1.00 0.00 4.00 460 25.00 83.04% Safe

10 Shyodanpura Minor 6.29 864 1.50 0.72 0.34 3.65 4.87 2.97 0.70 4.43 466 29.17 77.48% Safe

11 Chandlai Minor 4.53 610 3.68 1.86 1.10 4.29 2.55 0.23 0.01 2.14 594 12.50 56.25% Critical

12 Doulat Sagar Minor 4.50 559 2.39 1.10 0.38 2.86 2.15 1.22 0.09 2.41 499 14.29 47.80% Hyper Critical

13 Bidoli Minor 4.11 635 3.68 1.86 1.10 4.29 3.68 3.32 1.84 3.67 565 25.00 89.62% Safe

14 Panwad Sagar Minor 4.06 584 0.23 0.13 0.03 0.39 2.55 0.73 0.38 2.49 368 38.89 62.76% Critical

15 Thanwla Minor 3.41 584 0.48 0.26 0.07 0.80 1.93 18.07 0.27 1.97 367 27.27 56.55% Critical

16

Ghareda

Sagar Minor 2.88 508 2.38 1.38 0.60 2.70 2.38 1.33 0.93 2.16 641 31.58 82.51% Safe

17

Ramsagar L.

Harisingh Minor 2.86 569 0.33 0.11 0.03 0.69 2.07 1.20 0.65 1.98 331 40.00 72.28% Critical

18 Soonthra Minor 2.78 711 1.45 0.59 0.15 1.94 2.27 1.92 0.89 2.21 514 16.67 81.63% Safe

19 Kumhariya Minor 2.69 304* 0.23 0.09 0.01 0.37 2.52 2.00 1.23 2.29 304 30.77 93.68% Safe

20 Thikariya Minor 3.68 306* 0.33 0.14 0.02 0.52 3.71 3.12 2.12 3.32 306 70.59 100.77% Safe

21 Ramsagar G. Minor 2.46 508 0.43 0.15 0.03 0.90 0.51 0.29 0.12 0.91 331 20.00 20.69% Hyper Critical

22 Halolaw K. Minor 2.10 508 2.38 1.38 0.60 2.70 2.10 0.95 0.40 1.65 403 57.14 100% Safe

23 Matholao Minor 2.03 719 0.92 0.45 0.20 1.23 0.33 0.10 0.04 0.58 565 0.00 16.05% Hyper Critical

24

Mohamadgar

h Minor 1.95 940 0.53 0.22 0.06 0.71 1.87 0.96 0.41 1.47 514 46.15 95.65% Safe

25 Dhibru Sagar Minor 1.79 660 0.76 0.23 0.14 2.31 1.53 1.50 0.28 1.45 403 28.57 85.47% Safe

26 Bhanpura Minor 1.76 514* 0.85 0.35 0.09 1.14 1.54 0.79 0.20 1.31 514 33.33 87.90% Safe


89

27

Bhavalpur

Kerwaliya Minor 1.74 402* 0.36 0.11 0.06 0.52 1.54 1.09 0.48 1.45 403 25.00 88.53% Safe

28

Mansagar

Arnia Minor 1.64 584 0.35 0.19 0.05 0.59 1.64 1.30 0.18 1.34 367 64.71 100% Safe

29 Nasirda Minor 1.61 584 0.23 0.13 0.03 0.39 1.36 0.92 0.25 1.22 367 44.44 84.57% Safe

30 Duni Sagar Minor 1.42 584 0.68 0.41 0.34 0.74 0.65 0.48 0.06 0.77 576 20.00 46.00% Hyper Critical

31

Sangrampura

Newariya Minor 1.36 584 0.18 0.08 0.02 0.23 0.77 0.40 0.19 0.75 368 18.75 56.46% Critical

32 Dudi Sagar Minor 0.42 711 0.53 0.22 0.06 0.71 0.33 0.18 0.08 0.30 514 50.00 78.00% Safe

Safe: Actual yield (50% dep.) in % of design capacity: 75-100%= 17 dams

Critical: Actual yield (50% dep.) in % of design capacity: 50-74%= 6 dams

Hyper Critical: Actual yield (50% dep.) in % of design capacity: <50%= 9 dams

15 dams out of 32 dams of Tonk district fall under critical and hyper critical stage as per above mentioned interval

scale. We can say that around 50% of the dams may be considered safe, which are receiving more than 75% of their design

capacity at 50% dependability. Average observed rainfall in the district is less than the average design rainfall, which is one of

the reasons for less inflow.

Data are normalized with respect to design capacity of dams. RMSE and MAPE values are higher and R-values are

very low (<0.2), showing no correlation between computed and theoretical values (Table 4.4). Computed t-values are less than

critical t-value at 5% level of significance (2.07) i.e. accept null hypothesis (H0: R=0). NSE values are negative, showing that

average of the observed value is better predictor than the theoretical value. Simulation results are not in correlation with the

observed values.

Table 4.4 Performance statistics for the dams of Tonk district

Dependability RMSE MAPE (%) R T-Value NSE

50% 0.47 57.93 0.15 0.83 -2.40

75% 0.36 95.13 0.07 0.38 -1.13

90% 0.18 370.40 0.04 0.22 -0.64

Mean 0.49 63.66 0.03 0.16 -6.36


90


Critical year separates the pre-disturbance and post-disturbance two non-overlapping

epochs. Determination of critical year is an essential requirement for investigation of

inflow behaviour of the dams.

4.3.1 Determination of Possible Critical Years: Time Series Analysis

In the first step of determination of critical year, possible critical years are identified.

Possible critical years have been identified with the help of time series analysis by trend

elimination using least square method as shown in Table 4.5.

The trend equation U = a + bt has been derived as

Inflow = (64.77) + (-0.27) x Time

Where U= Inflow, a = 64.77, b = -0.27 and t= time

Table 4.5 Time series analysis: Dams of Rajasthan state (Trend elimination using least

square method)

Year Inflow, U

(%) t tUi t*t Trend Elimination

of trend Possible critical

years

1990 71.01 -11 -781.11 121 67.72 3.28

1991 78.53 -10 -785.3 100 67.45 11.07

1992 66.16 -9 -595.44 81 67.18 -1.02 ***

1993 62.6 -8 -500.8 64 66.91 -4.31

1994 85.76 -7 -600.32 49 66.65 19.1 ***

1995 77.6 -6 -465.6 36 66.38 11.21

1996 76.7 -5 -383.5 25 66.11 10.58

1997 71.09 -4 -284.36 16 65.84 5.24

1998 57.78 -3 -173.34 9 65.57 -7.79

1999 43.58 -2 -87.16 4 65.3 -21.72

2000 42.03 -1 -42.03 1 65.04 -23.01

2001 58.4 0 0 0 64.77 -6.37 ***

2002 30.87 1 30.87 1 64.50 -33.63

2003 58.08 2 116.16 4 64.23 -6.15 ***

2004 71.01 3 213.03 9 63.96 7.04

2005 71 4 284 16 63.69 7.3

2006 83.58 5 417.9 25 63.43 20.14

2007 69.72 6 418.32 36 63.16 6.55 ***

2008 56.2 7 393.4 49 62.89 -6.69

2009 41.27 8 330.16 64 62.62 -21.35

2010 51.52 9 463.68 81 62.35 -10.83 ***

2011 73.35 10 733.5 100 62.09 11.25

2012 76.78 11 844.58 121 61.82 14.95

2013 76.71 12 920.52 144 61.55 15.15

Sum 1551.33 12 467.16 1156 1551.33


91

Exceptional periods are marked ***, which are considered as possible critical years, when

drastic changes were observed. In this analysis year 1992, 1994, 2001, 2003, 2007, and

2010 are identified as possible years (Table 4.5).

4.3.2 Determination of Critical Years: Sequential Cluster Analysis

It is essential to determine the critical year, which divides two consecutive non-

overlapping epochs, pre-disturbance and post-disturbance. The final critical year can be

obtained by applying Sequential Cluster Analysis on the possible critical years which

have been identified in Table 4.5. The minimum sum of squared deviations for the

hydrological series before and after the possible critical years, gives the unique critical

year (Table 4.6). The method of computation has been described in section 3.3.5.

Table 4.6 Sequential cluster analysis: Dams of Rajasthan state

S.N. Year Inflow

(%)

Sequential Cluster Analysis

1992 1994 2001 2003 2007 2010

1 1990 71.0 0.8 3.2 26.1 65.7 32.6 62.5

2 1991 78.5 44.0 32.9 159.6 244.4 175.1 238.2

3 1992 66.2 32.9 44.1 0.1 10.6 0.7 9.4

4 1993 62.6 1.0 104.0 10.9 0.1 7.3 0.2

5 1994 85.8 491.1 168.0 394.5 522.7 418.7 513.6

6 1995 77.6 195.9 227.9 136.8 216.0 151.2 210.1

7 1996 76.7 171.5 201.6 116.6 190.4 129.9 184.9

8 1997 71.1 56.1 73.8 26.9 67.1 33.5 63.9

9 1998 57.8 33.9 22.3 66.0 26.2 56.6 28.3

10 1999 43.6 400.8 357.9 498.1 373.2 471.7 381.0

11 2000 42.0 465.4 419.1 569.9 435.7 541.6 444.1

12 2001 58.4 27.0 16.8 56.2 20.2 47.6 22.1

13 2002 30.9 1071 1000.3 1051.5 1025.8 1185.3 1038.6

14 2003 58.1 30.5 19.6 27.3 23.3 52.2 25.2

15 2004 71.0 54.9 72.4 59.4 15.3 32.6 62.6

16 2005 71.0 54.7 72.2 59.3 15.2 32.5 62.4

17 2006 83.6 399.4 444.5 411.4 271.7 334.3 419.6

18 2007 69.7 37.5 52.1 41.2 6.9 50.7 43.8

19 2008 56.2 54.7 39.7 50.4 118.7 40.9 47.6

20 2009 41.3 498.8 450.9 485.5 667.4 455.2 476.7

21 2010 51.5 145.9 120.6 138.8 242.7 122.8 134.1

22 2011 73.3 95.0 117.6 100.9 39.0 115.5 5.1

23 2012 76.8 173.6 203.8 181.6 93.6 201.0 1.4

24 2013 76.7 171.7 201.8 179.7 92.3 199.0 1.2

Sum

4708 4467 4849 4784 4888 4477

The minimum sum of squared deviations is correspond to the year 1994.

Therefore, the year 1994 is considered as the critical year for the hydrological behavior


92

of the dams of Rajasthan state. This shows that, some disturbances have taken place after

1994 which is attributed to low inflow to the dams. Determination of critical year will

help in policy formulation for an effective water resources planning and management of

the state.

4.4 Various Parameters and their Trends

The declining trend of inflow to the dams of Rajasthan State has been tested. The critical

year of initiation of post-disturbance epoch is determined as 1994. Climate change and

global warming are posing a serious threat to the water resource availability. Climatic

parameters such as average daily minimum and maximum temperature, precipitation,

wind speed, relative humidity and solar radiation are studied and graphs are plotted to

determine the variation of these parameters with time (Fig. 4.4 and 4.5). Various

parameters which are considered responsible for surface runoff are to be analyzed and

discussed. Trends of various factors are plotted and shown in fig 4.4 to 4.11.

4.4.1 Rainfall and Other Climatic Parameters

Accurate and correct rainfall characterization is an important tool for water related system

design and management. Although the spatial distribution of the change in precipitation

character is uncertain, it would mean greater risk of both dry spells and floods for some

region even though annual precipitation totals may increase slightly (Dourte et al. 2012).

Temporal variation of average annual precipitation of the state for long time series (1901-

2013) is on slightly increasing trend (Fig. 4.4 a). The trend of rainy days for simulation

period (1990-2013) is also slightly increasing and plotted along with the rainfall trend in

Fig. 4.4 (b). It infers that rainy days and annual rainfall has a positive and good

correlation which is shown in Fig. 4.4 (c). For a constant amount of annual rainfall (e.g.

700-715mm), inflow has a negative correlation with rainy days (Fig. 4.4 d). This infers

that the annual rainfall spreads over more rainy days and gives lesser inflow and more

infiltration. Temporal distribution of monthly rainfall shows that rainy season remains

during only four months in a year i.e. June to September (Fig. 4.4 e). There is a spatial

distribution of average annual rainfall over the state ranging from 190mm in Jaisalmer to

1000mm in Banswara (Fig. 4.4 f) therefore, it becomes essential to judiciously plan and

manage the available water resources within the state. Rainfall distribution is following


93

the Gaussian distribution. Highest frequency of annual rainfall of the state ranges between

500-600mm as shown in Fig. 4.5 (g). For constant amount of annual rainfall (e.g. 700-

715mm), inflow decreases with increase of rainydays.

(a) Rainfall trend (1901-2013)

(b) Rainfall and rainy days trend (1990-2013)

0

200

400

600

800

1000

1200

1900 1920 1940 1960 1980 2000 2020

Rain

fall

(m

m)

Rainfall trend (1901-2013)

10

20

30

40

50

60

70

80

90

100

0

100

200

300

400

500

600

700

800

900

1985 1990 1995 2000 2005 2010 2015

Ra

iny

da

ys

(nos.

)

Ra

infa

ll (

mm

)

Rainfall and rainy days trend (1990-2013)

Rainfall trend Rainy days Linear (Rainfall trend) Linear (Rainy days)


94

(c) Rainy days and rainfall

(d) Rainy days and inflow at constant rainfall

(e) Monthly rainfall distribution

R² = 0.5693

0

200

400

600

800

1000

0 10 20 30 40 50 60

Rain

fall

(m

m)

Rainydays (nos.)

Rainy days and rainfall

Rainy days and rainfall Linear (Rainy days and rainfall)

R² = 0.361

30

40

50

60

70

80

90

100

20 30 40 50 60

Infl

ow

(%)

Rainy days (nos.)

Rainydays-Inflow at constant annual rainfall (700-715mm)

Rainydays-Inflow at Constant rainfall

Linear (Rainydays-Inflow at Constant rainfall)

0

50

100

150

200

250

JAN

FE

B

MA

R

AP

R

MA

Y

JUN

JUL

AU

G

SE

PT

OC

T

NO

V

DE

C

Rain

fall

(m

m)

Month

Monthly rainfall distribution of Rajasthan state


95

(f) District wise average annual rainfall

(g) Frequency distribution of rainfall

Fig. 4.4 Rainfall trend and its distribution (Rajasthan)

The Rainfall trend of the state varies significantly over the years and during the

year, showing the temporal variation of rainfall. It also varies significantly between

various districts, showing the spatial variation of rainfall (Fig. 4.4). We proposed one

way ANOVA to study the impact of districts and year on the annual rainfall. Table (a)

shows that impact of year on annual rainfall was highly significant (p value <0.01),

calculated value of F= 7.705 >F (tabulated) at df= 111, 3341 at 1% level of significance.

Table (b) shows that impact of districts on annual rainfall was also significant (p value <

0.01) and calculated value of F=95.136 >F (tabulated) at df= 32, 3420 at 1% level of

significance.

0

200

400

600

800

1000

1200

Rain

fall

(m

m)

District

District wise average annual rainfall

05

10152025303540

10

0-2

00

20

0-3

00

30

0-4

00

40

0-5

00

50

0-6

00

60

0-7

00

70

0-8

00

80

0-9

00

90

0-1

00

0

10

00-1

10

0

Freq

uen

ty

Rainfall (mm)

Frequency distribution of rainfall over 113 years (1901-2013)


96

Table (a) One way ANOVA using year as factor

Sum of Squares df Mean square F Significance

Between Groups 68690485 111 618833.198 7.705 0.000

Within Groups 2.68E+08 3341 80314.481

Total 3.37E+08 3452

Table (b) One way ANOVA using district as factor

Sum of Squares df Mean square F Significance

Between Groups 1.59E+08 32 4959945.535 95.136 0.000

Within Groups 1.78E+08 3420 52135.353

Total 3.37E+08 3452

The impact of global climate change is making the precipitation of rainfall pattern

quite uncertain (Kar 2011). Climate change challenges existing water resources

management practices by adding uncertainty (Draper and Kundell 2007).

Intergovernmental Panel on Climate Change (IPCC 2001) as described by Iglesias et al.

2007 states that climate change projections derived from global climate models driven by

socio-economic scenario results in an increase in temperature of 1.5 to 3.6oC and a

precipitation decrease of about 10 to 20% by 2050. This may lead to increased likelihood

of droughts and variability of precipitation in time-space, and intensity. That would

directly influence water resources availability. As described above the importance of

climatic parameters; trends of daily max. and min. temperature, wind speed, relative

humidity and solar radiation have been plotted in Fig. 4.5.


97

(a) Mean daily max. temp. (b) Mean of daily min. temp.

(c) Wind speed (d) Relative Humidity

(e) Solar radiation

Fig. 4.5 Trends of other climatic factors (Rajasthan)

31

32

33

34

35

36

1980 1990 2000 2010 2020

Tem

p. (

0 C)

Mean of daily max. temp.

17

18

19

20

21

1980 1990 2000 2010 2020

Tem

p. (

0 C)

Mean of daily min. temp.

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

1980 1990 2000 2010 2020

Win

d s

peed

(m

/sec)

Mean of daily Wind speed

trend

0.30

0.35

0.40

0.45

0.50

0.55

0.60

1980 1990 2000 2010 2020

RH

(fr

acti

on

)

Mean of daily RH trend

18

19

19

20

20

21

1980 1990 2000 2010 2020

Sola

r ra

dia

tio

n (M

J/m

2)

Mean of daily solar radiation

trend


98

There is an increasing trend of the mean of daily maximum temperature, daily

relative humidity, and daily solar radiation while a slightly decreasing trend of the mean

of daily minimum temperature and daily wind speed. Increased daily maximum

temperature and solar radiation cause evaporation of soil moisture thereby may cause

increased infiltration and reduced inflow. Trends and rate of changes of various climatic

parameters are listed in Table 4.8.


Land use data from 1957-2013 have been plotted in Fig. 4.6. A comparison has been

made in Fig. 4.6 and 4.7 that contributing area is decreasing while crop area is increasing.

This infers that the increasing crop area results in decreasing contributing area for runoff,

thereby less water inflow to the dams. Land holding data have been presented for the

available duration. Data of all the factors related to the study of the state are presented

from 1990 to 2013 as per the availability of the data of all the factors.

Fig. 4.6 Land use (Contributing effect): Rajasthan

The mean of daily data has been analyzed for the sake of getting the trends of climatic

parameters and further correlating them with the inflow. A detailed analysis of land use

changes has been presented for Rajasthan state for the data available from the year 1957

onwards (Fig. 4.7).

0

10

20

30

40

50

60

1957

1962

1967

1972

1977

1982

1987

1992

1997

2002

2007

2013

Area

(%

)

Land use trend (Contributing effect)


99

(a)

(b)

(c)

0

50

100

150

200

250

300

1957

1962

1967

1972

1977

1982

1987

1992

1997

2002

2007

2013

Area (

000 s

qk

m)

Area sown (Rajasthan state)

Area sown more than onceNet area sown

0%

20%

40%

60%

80%

100%

1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007 2013

Area

(%

)

Land use changes (Rajasthan State)

Barren land Culturable waste Fallow land Non Ag. uses Forest and pasture Net area sown

0

50

100

150

200

250

300

1957

1962

1967

1972

1977

1982

1987

1992

1997

2002

2007

2013

Area

(0

00 s

qk

m)

Contributing area-reducing area (Rajasthan state) Contributing area

Reducing area

Linear (Contributing area)

Linear (Reducing area)


100

(d)

(e)

(f)

Fig. 4.7 Land use changes (1957-2013): Rajasthan

Agricultural area is increasing with time and population. It has increased from

124, 000 sqkm to 180,000 sqkm from 1957 to 2013. The area sown more than once is also

increasing; it has increased from around 10,000 sqkm to 65,000 sqkm (Fig. 4.7 a).

Irrigation area increased from around 20,000 sqkm to 63,000 sqkm from 1968 to 2006.

1.00

3.00

5.00

7.00

2.00

3.00

4.00

5.00

6.00

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

mil

lion

Average l

an

d h

old

ing (

ha

)

Average field size and number of fields:

Rajasthan state

Average field sizeNumber of fields

0%

20%

40%

60%

80%

100%

1970 1975 1980 1985 1990 1995 2000 2005

Area

(%

)

Land holdings (Area %):

Rajasthan State

Large fieldsMedium fieldsSemi-medium fieldsSmall fieldsMarginal fields

0.00

2.00

4.00

6.00

8.00

1970 1975 1980 1985 1990 1995 2000 2005

mil

lion

Land holdings (million):

Rajasthan State

Large fields >10 hect.Medium fields 4-10 hect.Semi-medium 2-4 hect.Small fields 1-2 hect.Marginal fields <1.0 hect.


101

The contribution of groundwater irrigation to the total irrigated area was around 10,000

sqkm in 1968 and 45,000 sqkm in 2006 (Fig. 4.8 a). With the development of electrical

facilities, irrigation from tube wells increased from 120 sqkm in 1968 to 19,000 sqkm in

2006 (Fig. 4.8 b). Due to this tremendous increase in agricultural areas, land use pattern

changed at a very fast pace. The increase in groundwater irrigation facilities resulted in

lowering of water table and increase in dark zones within the state.

Land utilization of the state is plotted in graphs as shown in Fig. 4.6 & 4.7 shows

that the land utilizations which are having reducing effect on the surface runoff viz.

agricultural, forest and infrastructural uses are increasing and the land utilization which is

having a contributing effect on the surface runoff viz. barren land, culturable waste, and

fallow land are decreasing with the passage of time (Fig. 4.7 b). The area which is having

contributing effect on surface runoff has decreased to half of the area which was available

in 1957 (Fig. 4.7 c). These types of land use changes, viz. agricultural, forest and

infrastructural uses of land increase the surface roughness, are not favorable to the surface

runoff. The total cropped area has increased from 137 (000 sqkm) to 245 (000 sqkm)

during 1957 to 2012. The double crop area has increased from 12.86 (000 sqkm) to 64.71

(000 sqkm) during this period.

Due to continuous partitions of the fields, the average size of the field is

decreasing, and the total number of fields is increasing with time. The average size of the

land holding has decreased from 5.46 ha to 3.38 ha from 1970 to 2005, and a total number

of fields has increased from 3.7 million to 6.2 million during this period (Fig. 4.7 d). The

percentage area of large fields is decreasing while under marginal, small and semi-

medium category it is increasing. Accordingly the number of land holdings under

marginal, small and semi-medium category is increasing (Fig. 4.7 e,f). This reflects that

total number of fields are increasing because of partitions of large fields, thereby more

length of periphery boundaries increasing the surface roughness and decreasing the

resultant runoff to the reservoir.

4.4.3 Groundwater Level Changes

Agricultural area of the state is increasing due to increase in irrigation facilities. Irrigation

by ground water sources increased at a fast pace than surface water sources (Fig. 4.8 a).

Underground water sources such as wells and tube wells increased due to the


102

development of digging and boring system along with rapid electrification (Fig. 4.8 b),

resulting in overexploitation of ground water.

(a) Sources of irrigation

(b) Various sources of irrigation

Fig. 4.8 Development of agricultural and irrigation facilities in Rajasthan

Over-exploitation of groundwater has resulted in lowering of ground water level

below ground level. Fluctuations of basin wise ground water level below ground level are

shown in Fig. 4.9 (a). Groundwater level changes in the state are shown in Fig. 4.9 (b).

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

Irrig

ati

on

('0

00 S

q.k

m)

Surface water and ground water irrigation

Irrigation by surface water sources Irrigation by ground water sources Total Irrigation

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

19

68

19

70

19

72

1974

19

76

19

78

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

1996

19

98

20

00

20

02

20

04

20

06

Irrig

ati

on

Area

('0

00

Sq

.km

)

Development of various sources of irrigation

WellsTube WellsCanalsOther SourcesTanks


103

(b) Basin wise GWL below GL

(b) Rajasthan

Fig. 4.9 Groundwater level changes: Rajasthan

0

5

10

15

20

25

30

35

40

45

50

Pre

2013

Pre

2012

Pre

2011

Pre

2010

Pre

2009

Pre

2008

Pre

2007

Pre

2006

Pre

2005

Pre

2004

Pre

2003

Pre

2002

Pre

2001

Pre

2000

Pre

19

99

Pre

1998

Pre

1997

Pre

19

96

Pre

1995

Pre

1994

Pre

19

93

Pre

1992

Pre

1991

Pre

1990

Pre

1989

Pre

1988

Pre

1987

Pre

1986

Pre

1985

Pre

1984

GW

L b

elo

w G

L (

m)

Shekhawati basin Ruparail basin Banganga basinGambhir basin parbati basin Sabi basinBanas basin Chambal basin Mahi basinSabarmati basin Luni basin West banas basinSukli basin Other nallah of Jalore Outside basin

10.0

12.0

14.0

16.0

18.0

20.0

22.0

24.0

26.0

19

84

19

85

19

86

19

87

19

88

19

89

19

90

19

91

19

92

1993

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

2002

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

2011

2012

20

13

GW

L b

elo

w G

L (

m)

Temporal variation of GWL below GL (Rajasthan state)

Pre-monsoon GWL Post-monsson GWL

Linear (Pre-monsoon GWL) Linear (Post-monsson GWL)


104

Basin wise fluctuations of ground water level below ground level shows that

Shekhawati basin is adversely affected by ground water withdrawal and showing the

deepest ground water level below ground level. A major part of Sikar and Jhunjhunu

districts lie in the Shekhawati basin. Water inflows to the dams of these districts are also

negligible. Looking to these adverse situations, it is planned to provide water for drinking,

irrigation and other necessary requirements from Yamuna water through the

transboundary management of surplus water through an interstate agreement between

Rajasthan and Haryana. Sikar district has also been covered in Model District Irrigation

Plan under Pradhan Mantri Krishi Sinchai Yojna (PMKSY). In the first phase, water is to

be received through Yamuna water agreement and in successive phases it is to be

received from Sharda-Yamuna Link. Major parts of Barmer, Churu, Ganganagar,

Hanumangarh, Jaisalmer, Jodhpur and Nagaur districts are falling under Outside Basin.

Though groundwater level below ground level in this basin is very low but it is not going

further low at accelerating rate as part of the basin is under Gang Canal and Indira Gandhi

Canal system.

Development of lift facilities through pump sets resulted in a tremendous increase

in irrigation from groundwater using wells and tube wells (Fig. 4.8 b). Overexploitation

of groundwater has resulted in only 25 blocks out of total 243 in the safe category in the

year 2011, which were 135 in the year 1998 (Table 4.7, Fig. 4.10). Tara Nagar of Churu

district and Khajuwala of Bikaner district have been declared as total saline blocks;

therefore, they have not been assessed by Central Ground Water Board (CGWB 2014).

Declining water table reduces runoff due to base flow and hence the inflow to the wetland

(Jain et al. 2008). Over exploitation of surface water results in many rivers dying, and

outflow amount decreasing drastically, and overexploitation of groundwater has also

induced the groundwater level to go down continuously and form several underground

funnels. Over-exploitation of water has greatly changed the transfers among precipitation,

surface water, soil water, and groundwater. As is seen in Hai He basin, the river is dying,

and funneled groundwater is increasing, and this once natural water cycle has evolved

into a semiartificial water cycle (Sang et al. 2010). Losses due to evaporation and water

withdrawal reduce the runoff towards the mouth by a factor 2.3. The runoff changes as

large as these are due to the increasing water withdrawal for irrigation (Kravtsova et al.


105

2009). It has been observed during the last five decade that, percentage groundwater

utilizations have almost doubled, and the surface water utilization has increased

manifold, causing a decline in the water table. The declining water table reduces

runoff due to baseflow and hence the inflow to the wetland (Jain et al. 2008). In

Rajasthan state also, over-exploitation of water has greatly changed the transfers among

precipitation, surface water, soil water, and groundwater. The rivers are dying and

funneled ground water is increasing leading to the diminishing base flow and decreasing

surface runoff.

Table 4.7 Scenario of Groundwater Development: Rajasthan Year Over-exploited blocks

(>100%) Critical blocks (90%-100%)

Semi-critical blocks (70%-90%)

Safe blocks (<70%)

1998 41 26 34 135

2004 102 82 27 25

2009 166 25 16 31

2011 172 24 20 25

Graphical presentation of Table 4.7 is shown in Fig. 4.10 in the form of stacked

bar chart. This shows that about 70% of the blocks of the state are over-exploited while

safe blocks are only around 10%.

Fig. 4.10 Scenario of groundwater development: Rajasthan

0

50

100

150

200

250

1998 2004 2009 2011

No.

of

blo

ck

s

Ground water scenario of the state

Safe block

Semi critical

Critical

Overexploited


106

Grond water development <70% may be considered as safe, 70% to 90% may be

considered as semi-critical, 90% to 100% may be considered as critical, and >100% is

considered as overexploited. Groundwater development is regularly increasing due to less

water availability in surface water resources. Districtwise groundwater development is

shown in appendix-10. Only 3 districts in safe, 2 districts in semi-critical, 2 districts in

critical and rest 27 districts lies under overexploited categories out of a total of 33

districts in the state. In whole, the complete state may be considered as overexploited.

Overall stage of groundwater development of the state is around 137% (CGWB 2014).

4.4.4 Population Growth and Trends

Land use and cultivation area are influenced by the population growth. The state

population is regularly increasing. However it is a good sign that decadal growth rate of

population is on decreasing trend after the year 1981 (Fig. 4.11 a). Country population

and its decadal growth rate are also on the same trend (Fig. 4.11 b). The percentage share

of the state population in country population is on increasing trend (Fig. 4.11 c).

(a) State population and its growth

10

.3

11

.0

10

.3

11

.7

13

.9

16

.0

20

.2

25

.8

34

.3 44

.0 56

.5 68

.6

-10.0

-5.0

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

1901

1911

1921

1931

1941

1951

1961

1971

1981

1991

2001

2011

2021

2031

2041

2051

Deca

da

l g

row

th r

ate

%

Mil

lion

s

Population of the State and Decadal growth rate %

Population

Decadal growth %

Poly. (Population)

Poly. (Decadal growth %)


107

(b) Country population and its growth

(c) State population share with India

(d) Per capita water availability in the state

Fig. 4.11 Population and its growth: Rajasthan

-5.0

0.0

5.0

10.0

15.0

20.0

25.0

30.0

0.0200.0400.0600.0800.0

1000.01200.01400.01600.01800.02000.02200.02400.0

1901

1911

1921

1931

1941

1951

1961

1971

1981

1991

2001

2011

2021

2031

2041

2051

Deca

da

l g

row

th r

ate

%

Mil

lion

s

Population of India and Decadal growth rate % Population

Decadal growth %

Poly. (Population)

Poly. (Decadal growth %)

y = 0.0002x2 - 0.5937x + 572.47

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

19

01

19

11

1921

19

31

1941

19

51

19

61

19

71

19

81

19

91

20

01

20

11

20

21

2031

20

41

2051

% S

ha

re

State Population: % share in India

% share in IndiaPoly. (% share in India)

0200400600800

1000120014001600180020002200240026002800

19

11 -

1920

1921 -

1930

1931 -

1940

1941 -

1950

1951 -

1960

1961 -

1970

1971 -

1980

19

81 -

1990

1991 -

2000

2001 -

2010

Cu

m/y

ear

Per capita water availability

Per capita water availability

Water scarcity level

Absolute water scarcity level

Linear (Per capita water availability)


108

Fig. 4.11 (‘c) shows that the impact of population growth on natural resources will

be more severe than the average condition of the country. This impact on water resources

availability will be more severe in future because per capita water availability in the state

has gone down below the level of water scarcity (1000 cum per capita per year) and it is

touching to the level of absolute water scarcity (500 cum per capita per year) (Fig. 4.11

d). There is an alarm to adopt most efficient practices of water resources planning and

management in the state. Han et al. (2012) also mention that available water resources per

capita are decreasing in river basins around the world.

4.4.5 Various Factors, Trend Lines and their Rate of Changes

Trend lines showed in Fig. 4.4 to 4.11give an idea about the various factors, whether they

are on increasing or decreasing trend and at what rate. The results are summarized in

Table 4.8.

Table 4.8 Various factors, trend lines and their rate of change

S.N. Factor Trend Rate of Change

1 Rainfall 1901-2013 (mm) Increasing 10 mm in 25 yrs.

2 Rainfall 1990-2013 (mm) Increasing 10mm in 22 yrs.

3 Rainy days/ year 1990-2013 (nos.) Increasing 1 day in 6 yrs.

4 Mean of daily max. temp. (0C) Increasing 1 deg. in 500 yrs.

5 Mean of daily min. temp. (0C) Decreasing 1 deg. in 77 yrs.

6 Mean of daily wind speed (m/sec) Decreasing 1 m/sec in 143 yrs.

7 Mean of daily RH (fraction) Constant NA

8 Mean of daily solar radiation (MJ/m2) Increasing 1MJ/m2 in 40 yrs.

9 Land use (Contributing effect) (%) Decreasing 1% in 3 yrs.

10 Cultivation area (%) Increasing 1.37% in 2 yrs.

11 Groundwater level below ground level (m) Increasing 1m in 6 yrs.

12 State population (million) Increasing 80 million in 2021

13 State population decadal growth Decreasing 15-20% in 2021

14 Country population Increasing 1400 million in 2021

15 Country population decadal growth Decreasing 15% in 2021

16 State population share with country Increasing 6.2% in 2021


109

Rainfall and rainy days during 1990-2013 are on increasing trends (Fig. 4.4 b).

Rainy days have positive correlations with annual rainfall and inflow if plotted

independently (Fig. 4.4 c and 4.12 b). A graph between rainy days and inflow for the

nearly constant amount of annual rainfall (700-710mm) has been plotted in Fig. 4.4 (d).

This shows a decreasing trend of inflow with increasing rainy days for a constant rainfall.

This infers that precipitations are shifting towards fewer common higher intensity storms

and more light and moderate events resulting in lesser runoff. This pattern shows that

nearly constant amount of monsoon rainfall is spreading over the longer time

duration than earlier, which may result in reduced percentage inflow.

Land use having a contributing effect is on decreasing trend. Cultivation area,

state population, state population share with the country, and groundwater level below

ground level is on increasing trend. A stable tendency towards a decrease in water

resources can be seen in recent decades, which is largely due to the intense economic

activities on watersheds (Shiklomanov et al. 2011). Irrigation systems, three dams and

seven ponds built recent decades for agricultural irrigation and domestic use made the

major impact on lowering the lake levels because they derive water from the river for

human use upstream of the lake’s catchments. Population growth, rising water

consumption for agricultural and domestic purposes and building dams has led to lake

levels declining. The change of lake levels might depend more on anthropogenic factors

than on climate factors (Yildirim et al. 2011).

4.4.6 Correlation Matrix: Rajasthan

A 14x14 correlation matrix has been developed. Mutual correlations of most of the

factors are insignificant, being quite low. But, some factors have significant mutual

correlations. Significant correlations at 2-tailed test at 1% and 5% level of significance

has been shown by * and **. However, the matrix has been derived to show the mutual

correlations of all the factors with each other (Table 4.9), for the sake of mutual

dependence. This matrix can be utilized for determining the sensitivity of each parameter

with other remaining parameters.


110

Correlations

1.000 -.130 -.480* .999** .915** -.059 .114 .020 -.165 -.404 .125 .584** .366 .824**

. .545 .018 .000 .000 .786 .596 .926 .442 .050 .559 .003 .079 .000

24 24 24 24 24 24 24 24 24 24 24 24 24 24

-.130 1.000 -.097 -.108 -.264 .685** .326 -.738** -.518** -.696** .577** -.502* .129 -.106

.545 . .651 .614 .213 .000 .120 .000 .009 .000 .003 .013 .547 .621

24 24 24 24 24 24 24 24 24 24 24 24 24 24

-.480* -.097 1.000 -.493* -.385 -.049 .048 .157 .013 .388 -.242 -.193 -.991** -.734**

.018 .651 . .014 .063 .819 .823 .464 .952 .061 .255 .367 .000 .000

24 24 24 24 24 24 24 24 24 24 24 24 24 24

.999** -.108 -.493* 1.000 .906** -.040 .132 -.009 -.176 -.424* .154 .571** .380 .830**

.000 .614 .014 . .000 .854 .539 .968 .411 .039 .473 .004 .067 .000

24 24 24 24 24 24 24 24 24 24 24 24 24 24

.915** -.264 -.385 .906** 1.000 -.250 -.003 .178 -.180 -.244 -.082 .701** .281 .751**

.000 .213 .063 .000 . .239 .990 .406 .401 .252 .704 .000 .184 .000

24 24 24 24 24 24 24 24 24 24 24 24 24 24

-.059 .685** -.049 -.040 -.250 1.000 .715** -.746** -.272 -.511* .800** -.549** .071 .060

.786 .000 .819 .854 .239 . .000 .000 .199 .011 .000 .005 .742 .781

24 24 24 24 24 24 24 24 24 24 24 24 24 24

.114 .326 .048 .132 -.003 .715** 1.000 -.742** -.375 -.370 .810** -.274 -.060 .139

.596 .120 .823 .539 .990 .000 . .000 .071 .075 .000 .196 .782 .518

24 24 24 24 24 24 24 24 24 24 24 24 24 24

.020 -.738** .157 -.009 .178 -.746** -.742** 1.000 .650** .591** -.900** .498* -.177 -.059

.926 .000 .464 .968 .406 .000 .000 . .001 .002 .000 .013 .408 .783

24 24 24 24 24 24 24 24 24 24 24 24 24 24

-.165 -.518** .013 -.176 -.180 -.272 -.375 .650** 1.000 .521** -.336 -.001 -.001 .001

.442 .009 .952 .411 .401 .199 .071 .001 . .009 .108 .996 .996 .996

24 24 24 24 24 24 24 24 24 24 24 24 24 24

-.404 -.696** .388 -.424* -.244 -.511* -.370 .591** .521** 1.000 -.545** .011 -.358 -.419*

.050 .000 .061 .039 .252 .011 .075 .002 .009 . .006 .959 .086 .041

24 24 24 24 24 24 24 24 24 24 24 24 24 24

.125 .577** -.242 .154 -.082 .800** .810** -.900** -.336 -.545** 1.000 -.444* .245 .286

.559 .003 .255 .473 .704 .000 .000 .000 .108 .006 . .030 .249 .176

24 24 24 24 24 24 24 24 24 24 24 24 24 24

.584** -.502* -.193 .571** .701** -.549** -.274 .498* -.001 .011 -.444* 1.000 .124 .503*

.003 .013 .367 .004 .000 .005 .196 .013 .996 .959 .030 . .562 .012

24 24 24 24 24 24 24 24 24 24 24 24 24 24

.366 .129 -.991** .380 .281 .071 -.060 -.177 -.001 -.358 .245 .124 1.000 .665**

.079 .547 .000 .067 .184 .742 .782 .408 .996 .086 .249 .562 . .000

24 24 24 24 24 24 24 24 24 24 24 24 24 24

.824** -.106 -.734** .830** .751** .060 .139 -.059 .001 -.419* .286 .503* .665** 1.000

.000 .621 .000 .000 .000 .781 .518 .783 .996 .041 .176 .012 .000 .

24 24 24 24 24 24 24 24 24 24 24 24 24 24

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

YEAR

INFLOW

LULC

POPDEN

WTRTBL

RAINFALL

RDAYS

MAXTMP

MINTMP

WINDSPD

RELHUM

SOLAR

NA

ASMO

YEAR INFLOW LULC POPDEN WTRTBL RAINFALL RDAYS MAXTMP MINTMP WINDSPD RELHUM SOLAR NA ASMO

Correlation is signif icant at the 0.05 level (2-tailed).*.

Correlation is signif icant at the 0.01 level (2-tailed).**.

Table 4.9 Correlation Matrix: Rajasthan


111


Correlations of various components with inflow have been established after drawing the

trend line and regression equation.


To reduce the dimensionality of the proposed equation, linear regression analyses of

inflow (%) with various factors have been carried out independently, as shown in fig 4.12

and summarized in Table 4.10.

(a) Rainfall (b) Rainy days

(c) Land use (d) GWL Below GL

y = 0.0843x + 14.115

R² = 0.5056

0102030405060708090

100

0 500 1000

Infl

ow

(%

)

Rainfall (mm)

Rainfall-Inflow

y = 0.4284x + 50.21

R² = 0.1095

0102030405060708090

100

0 20 40 60

Infl

ow

(%

)

Rainy days

Rainy days-Inflow

y = -0.277x + 74.04

R² = 0.00

0102030405060708090

100

20 25 30 35 40 45 50 55

Infl

ow

(%

)

Contributing land use (%)

Land use-Inlfow

y = -0.574x + 76.92

R² = 0.00

0102030405060708090

100

15 20 25 30

Infl

ow

(%

)

GWL BGL (m)

GWL BWL-Inflow regression


112

(e) Mean daily max. Temp. (f)Mean daily min. temp.

(g) Wind speed (h) Relative humidity

(i) Solar radiation (j) Population density

y = -11.532x + 453.5

R² = 0.5426 0

20

40

60

80

100

31.00 32.00 33.00 34.00 35.00 36.00

Infl

ow

(%

)

Mean of daily max. temp. (0C)

Mean daily max. temp.-Inflow

y = -13.501x + 320.45 R² = 0.2666

0

20

40

60

80

100

17 18 19 20 21

Infl

ow

(%

)

Mean of daily min. temp. (0C)

Mean daily min. temp.-Inflow

y = -73.989x + 277.45

R² = 0.4864 0

20

40

60

80

100

2.5 3.0 3.5

Infl

ow

(%

)

Wind speed (m/sec)

Wind speed-Inflow

y = 215.39x - 19.437

R² = 0.3485

0

20

40

60

80

100

0.30 0.35 0.40 0.45 0.50

Infl

ow

(%

)

Relative humidity

Relative humidity-Inflow

y = -14.982x + 352.86

R² = 0.2525 0

20

40

60

80

100

18.00 18.50 19.00 19.50 20.00 20.50

Infl

ow

(%

)

Mean of solar radiation (MJ/m2)

Mean daily solar radiation-

Inflow

y = -0.0582x + 74.448

R² = 0.012 0

20

40

60

80

100

100 150 200 250

Infl

ow

(%

)

Population density

Population density-Inflow


113

(k) Gross inflow-rainfall

Fig. 4.12 Cross plots: Between inflows and independent variables: Rajasthan

Several cross plots showing the relationship between independent variables and

inflows have been generated to develop empirical relations for the indirect estimation of

the output. These relations are obtained using statistical methods such as regression

analysis. However, although empirical equations are cost- effective and not too time-

consuming, they include uncertainties relating to the limited data available (Majdi et al.

2010). To determine the parameters affecting the inflow more than others, several cross

plots showing the relationship between independent variables and inflows (%) have been

generated for a total of 24 datasets (Fig. 4.12 a to J and Table 4.10). A regression plot

between gross inflow and rainfall has been developed in Fig. 4.12 (k) which shows a

highly significant correlation with t-value of 3.33 but less than the t-value of inflow (%)

and rainfall.

y = 7.8494x + 2192.6

R² = 0.3359

0

2000

4000

6000

8000

10000

12000

0 200 400 600 800 1000

Gross

In

flow

(M

CM

)

Rainfall (mm)

Rainfall-Gross inflow


114

Table 4.10 Correlation and regression of inflow (%) with various factors: Rajasthan

S. N.

Factor Regression

equation

Type of

correlation Coefficient of

correlation (R)

t-

value Strength of

correlation

1 Rainfall (mm) y = 0.084x + 14.11 Positive 0.71 4.74 HS

2 Rainy days (nos.) y = 0.428x + 50.21 Positive 0.33 1.64 NS

3 Land use (Cont. effect) NS - 0.08 0.39 NS

4 GWL BGL (m) NS - 0.05 0.26 NS

5 Population density NS - 0.11 0.52 NS

6 Mean of daily max. temp. (0C) y = -11.53x + 453.5 Negative 0.74 5.1 HS

7 Mean of daily min. temp. (0C) y = -13.50x + 320.4 Negative 0.52 2.82 S

8 Mean of daily wind speed (m/sec) y = -73.98x + 277.4 Negative 0.70 4.56 HS

9 Mean of daily RH (fraction) y = 215.3x - 19.43 Positive 0.59 3.43 HS

10 Mean of daily solar radiation

(MJ/m2)

y = -14.98x + 352.8 Negative 0.50 2.72 S

Here, NS=Not Significant, S= Significant, HS= Highly Significant

Here no. of years (n)=24<30, therefore, to determine correlations of inflows (%)

with the variables independently, t-tests have been applied. Here df=n-2=22, and

corresponding critical value of tc=2.074 and 2.819 at 5% and 1% level of significance.

Jain et al. (2008) developed a regression equation showing the relationship of runoff with

rainfall and depth to water table. For Rajasthan state as a whole, applying linear

regression on the available 24 years datasets, we find that rainy days and GWL BGL have

weak correlations with runoff. Land use and population density do not have any

correlation with inflow. Regression analysis of land use (contributing area) is showing a

negative correlation with inflow with R=0.08 and t-value=0.39 against the critical value

of tc=2.074 and 2.819 at 5% and 1% level of significance. Rainfall and other climate

parameters have moderate to strong correlations with inflow. This shows that land use

have no correlation with the inflows to the dams of the state as a whole and rainfall is

having the strong effect on the inflows i.e. inflows was less than the average values when

the rainfalls were lesser than the average rainfalls even if the contributing areas of land

use were more than the average value desired, as observed in years 1999, 2000, 2001, and

2002. Inflows during the years 2011, 2012, and 2013 improved even after the lesser

contributing area than the average because of more than average rainfall during these

years. This concludes that rainfall and other climatic factors are principal components for

the inflow to the dams of the state i.e. rainfall, mean daily max. temp., wind speed, and

relative humidity are considered as principal components.


115


Cosine Amplitude Method of sensitivity analysis is employed to find out the principal

components and their relative importance. By relative influence value (Rij) computed by

CAM (Appendix-8), the relative importance of the four factors is mentioned as below:

(1) Rainfall= 0.96

(2) Mean of daily Relative humidity= 0.92

(3) Mean of daily wind speed= 0.73

(4) Mean of daily max. temp.= 0.66

Fig. 4.13 Relative Importance of the factors responsible for inflow (Rajasthan): CAM

Rainfall is the principal factor governing the inflow to the dams of the state as a

whole. Other climatic parameters like relative humidity, wind speed and mean of daily

max temperature also influence the inflow. Percentage effect of each factor on inflow can

be determined by the ratio ij

ij

R

R ;

Sensitivity analysis shows less water inflow to the dams of the Rajasthan state due to the

precipitation is the main factor having 30% of the effect; relative humidity is responsible

for 28% of the effect, wind speed 22% of the effect, and mean daily max. temp. has 20%

effect on the inflow.

0.96 0.92

0.73 0.66

0.00

0.20

0.40

0.60

0.80

1.00

1.20

Rainfall Relative humidity Wind speed mean daily max

temp.

Rela

tive in

flu

en

ce (

Rij)

Factor

Relative influence (CAM): Rajasthan


116


RAMGARH DAM





Series Analysis


Cluster Analysis

Lysis

5.4 Various Parameters and Their Trends






of Changes









117

CHAPTER-5

TEMPORAL VARIATION OF INFLOW TO THE RAMGARH DAM

Year wise inflow data of Ramgarh dam have been shown in Table 5.1. Temporal

variation of inflow to the Ramgarh dam has been shown in Fig. 5.1 It is evident that the

water inflow trend to the dam has tremendously declined. Inflow to the dam was

satisfactory up to 1996-1997 which has reduced to around nil inflow during 2009-2014.

The average inflow by last 32 years data (1983-2014) is 12.08 MCM against the design

capacity of 75 MCM. During the last 32 years, Ramgarh dam never received water equal

to or more than its design capacity. Therefore, performance dependability of the dam may

be considered as zero. However, if we compute the yield at different dependability’s, the

dam yields only 4.67 MCM and 0.87 MCM at 50% and 75% dependability’s respectively.

Dependability of the dam at design capacity (75 MCM) is nil. The theoretical yield has

been computed using Strange’s Table considering the catchment as ‘bad.' Theoretical

yield, actual inflow, and weighted rainfall have been plotted against the successive years.

Fig. 5.1 Temporal variation of water inflow to the Ramgarh dam

0

100

200

300

400

500

600

700

800

900

1000

0

20

40

60

80

100

120

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Ra

infa

ll (

mm

)

Infl

ow

(M

CM

)

Year

Inflow

Theoretical yield

Rainfall

Linear (Inflow)

Linear (Theoretical yield)

Linear (Rainfall)


118

Theoretical inflows have been computed to compare the actual inflows with the

anticipated inflows as per rainfall and to check whether the method of computation

prevailing in the department holds good or not (section 5.2).

Table 5.1 Year wise inflow to the Ramgarh dam

S.N. Year Rainfal

l (mm)

Theoretical

yield (MCM)

Observed

Inflow

(MCM)

Observed Inflow

in Descending

Order

Dn

1 1983 654 51.37 55.46 55.46 3.0

2 1984 413 16.39 08.37 53.09 6.1

3 1985 566 36.22 40.46 49.66 9.1

4 1986 305 06.83 20.55 40.46 12.1

5 1987 210 02.29 04.55 28.99 15.2

6 1988 547 33.47 20.25 27.78 18.2

7 1989 454 21.14 00.91 20.55 21.2

8 1990 451 20.74 01.39 20.25 24.2

9 1991 302 06.67 04.79 15.72 27.3

10 1992 577 37.91 28.99 14.23 30.3

11 1993 606 42.93 15.72 08.37 33.3

12 1994 502 27.30 05.88 08.07 36.4

13 1995 856 95.23 49.66 07.34 39.4

14 1996 862 96.59 53.09 07.31 42.4

15 1997 541 32.64 27.78 05.88 45.5

16 1998 558 35.02 14.23 04.79 48.5

17 1999 446 20.17 08.07 04.55 51.5

18 2000 402 15.30 03.53 03.53 54.5

19 2001 364 11.65 01.93 01.93 57.6

20 2002 223 02.72 00.05 01.86 60.6

21 2003 922 112.33 07.34 01.71 63.6

22 2004 547 33.54 01.71 01.39 66.7

23 2005 856 95.24 07.31 01.07 69.7

24 2006 432 18.44 01.86 0.91 72.7

25 2007 412 16.24 0.86 0.86 75.8

16 2008 847 92.95 0.77 0.77 78.8

27 2009 361 11.41 0.00 0.05 81.8

28 2010 868 98.21 1.07 0.00 84.8

29 2011 721 64.33 0.00 0.00 87.9

30 2012 632 47.40 0.00 0.00 90.9

31 2013 696 59.37 0.00 0.00 93.9

32 2014 497 26.60 0.00 0.00 97.0

Average= 40.27 12.08


119

Graph of three-time segments 1983-1990, 1991-2000 and 2001-2014 (Fig. 5.2)

shows a tremendous reduction of inflow at the same quantum of monsoon rainfall. The

figure can also be used to predict the maximum probable inflow for a certain amount of

monsoon rainfall using trend line of 2001-2014.

Fig. 5.2 Water Inflow trend for different time segments: Ramgarh dam

Fig. 5.3 Variations of inflow with dependability in Ramgarh dam

Inflows on dependability are plotted as shown in Fig. 5.3. The Graph shows that

there was 0.00 MCM inflow at 84.80% reliability. As per code provisions, inflow at

100% reliability is considered for drinking water projects (IS 5477 Part-3:2002).

y = 0.094x - 23.304 R² = 0.464

y = 0.0943x - 32.133 R² = 0.8556

y = 0.0056x - 1.7322 R² = 0.2533

0.0

10.0

20.0

30.0

40.0

50.0

60.0

0 100 200 300 400 500 600 700 800 900 1000

Infl

ow

(M

CM

)

Rainfall (mm)

TREND 1983-1990TREND 1991-2000TREND 2001-2014Linear (TREND 1983-1990)Linear (TREND 1991-2000)Linear (TREND 2001-2014)

0.00

20.00

40.00

60.00

80.00

100.00

120.00

0.0 20.0 40.0 60.0 80.0 100.0 120.0

Infl

ow

(M

CM

)

Dependability (%)

Dependability-Inflow Curve

Observed

Simulated


120


Here, a total number of years are 32 i.e. n>30; therefore, Z-test has been applied.

Z-Test: Ho (Null Hypothesis): 75 MCM

H1 (Alternate Hypothesis): 75 MCM

Here n=32 (>30), therefore z-test has been applied.

08.12x ; S= 16.63; SE= 2.94;

z= 21.40;

Standard z value at 5% significance level= 1.96

z>zstd; therefore null hypothesis is rejected and alternate hypothesis is accepted, i.e.

Ramgarh dam is not getting the desired 75 MCM inflow of water. Trend of inflows infers

that the dam is getting less than the desired inflow.


On the basis of year wise inflow data (Table 5.1) hydrological performance of the dams is

measured as below:

Reliability: 0.00%

Resilience: 0.00%

Vulnerability: 83.89%

Maximum deficit: 100% (2009,2011,2012,2013,2014)

Sustainability: 0.00%

Results of performance analysis show that Ramgarh dam is highly vulnerable. As

per Danilyan et al. 2006, one of the major features of small rivers is a close correlation

between their runoff formation and watershed landscape, a property which makes them

heavily vulnerable when subject to intense economic development. Ploughing up of

floodplain lands, as well as other types of land treatment, leads, as a rule, to a decrease in

the annual and springtime runoff. The decrease in water flow disturbs the natural regime,

reduce the depth and rate of water flows, and causes the transformation of river channels

into chains of isolated pools and often a complete degradation of small rivers, the same

case exists in the Ramgarh dam catchment. The state of an ecological catastrophe, i.e., the

situation in which exogenous and endogenous processes of natural or anthropogenic

origin destroy the structural and functional organization of the system; the result of this


121

process is the death of the water body; no return of the system to the original state is

possible either in a normal way or through forced recultivation (Danil’Yan et al. 2006).

Theoretical values have been computed using the Thiessen polygon and Strange’s Table

considering ‘bad’ catchment and compared with the observed values. Statistical

parameters for measuring the performance of computation method of theoretical yield has

been shown in Table 5.2.

Table 5.2 Performance Statistics: Ramgarh dam

Statistical

Parameter MAE MAPE (%) R R2 t30 NSE RMSE RSR NMBE

Value 29.74 961280.41 0.30 0.09 1.72 -5.57 42.02 2.56 234.20

Optimal

value Lower value Lower value Unity Unity 2.04 Unity Lower value Zero Lower value

The value of the coefficient of correlation is 0.30, the corresponding t-value is

1.72, which is less than the critical value of tc = 2.042, at (32-2) =30 degree of freedom at

the 5% level of significance. This shows that there is no correlation between the observed

and computed values. Nash Sutcliff Efficiency (NSE) is a negative value, which shows

that the average of the observed values is a better indicator than the computed value.

Different inflow means can be computed for different rainfall events, and that may be

utilized for future prediction of inflow. RSR is also far beyond the optimal value. MAE,

MAPE, RMSE, and NMBE are very high. Therefore, it can be concluded that the method

of computation of theoretical values of inflows is not an acceptable method in this case.

During the last 32 years, Ramgarh dam never received water equal to or more than its

design capacity. Therefore, the performance dependability (reliability) of the dam may be

considered as zero. The dam yields only 4.88 MCM and 0.69 MCM at 50% and 75%

dependability respectively. Inflow to the Ramgarh dam was never remained above the

theoretical yield except the year 1986. Inflow always remained below the theoretical yield

after the year 1991, and it slashed after the year 2000 and touched to nearby zero level.

The rainfall trend over the catchment is increasing while the water inflow trend in the

Ramgarh dam has steeply declined as shown in Fig.5.1. The phenomenon was well

explained by Danil’Yan et al. (2006), the large variations in the hydrological regime are

typical of small rivers in urban territory because of radical changes in landscape due to


122

urbanization; many small rivers in such territory disappeared, therefore, the most

important problem in urban areas in the protection and rehabilitation of small river

resources rather than runoff withdrawal. Ramgarh dam is also situated on small river

Banganga, and the catchment of Ramgarh dam comes under the jurisdiction of Jaipur

Development Authority, i.e. in urban territory.


After testing the hypothesis and getting the inference that water inflow to the Ramgarh

dam has declined, it is essential to find out the critical year which divides the pre and

post-disturbance epochs. The sequential cluster analysis gives us the critical year out of

the possible years, which divides the pre and post-disturbance epochs.


Time Series Analysis and Sequential Cluster Analysis have been carried out for

determination of critical year. Time series analysis gives us an idea about the possible

critical years where trends are broken, and abrupt changes have taken place. The results

of the time series analysis are as shown in Table 5.3.

Table 5.3 Time series analysis: Ramgarh dam (Trend elimination using least square

method) S. N.

Year Inflow,

MCM t t*I t*t Trend

Elimination

of Trend

Possible

critical year

1 1983 55.45 -15 -831.8 225 26.95 28.49 2 1984 8.38 -14 -117.3 196 25.99 -17.61 ***

3 1985 40.47 -13 -526.1 169 25.03 15.43 4 1986 20.56 -12 -246.7 144 24.07 -3.51 5 1987 4.56 -11 -50.16 121 23.11 -18.55 6 1988 20.25 -10 -202.5 100 22.15 -1.9 7 1989 0.91 -9 -8.19 81 21.19 -20.28 8 1990 1.39 -8 -11.12 64 20.23 -18.84 9 1991 4.79 -7 -33.53 49 19.27 -14.48 10 1992 29 -6 -174 36 18.31 10.68 ***

11 1993 15.72 -5 -78.6 25 17.36 -1.64 12 1994 5.89 -4 -23.56 16 16.4 -10.51 13 1995 49.67 -3 -149 9 15.44 34.22 ***

14 1996 53.1 -2 -106.2 4 14.48 38.61 ***

15 1997 27.78 -1 -27.78 1 13.52 14.25 16 1998 14.22 0 0 0 12.56 1.65 ***

17 1999 8.07 1 8.07 1 11.6 -3.53 18 2000 3.54 2 7.08 4 10.64 -7.1 19 2001 1.93 3 5.79 9 9.68 -7.75 20 2002 0.06 4 0.24 16 8.72 -8.66 21 2003 7.34 5 36.7 25 7.76 -0.42


123

22 2004 1.73 6 10.38 36 6.8 -5.07 23 2005 7.31 7 51.17 49 5.84 1.46 ***

24 2006 1.87 8 14.96 64 4.88 -3.01 25 2007 0.85 9 7.65 81 3.92 -3.07 26 2008 0.76 10 7.6 100 2.96 -2.2 27 2009 0 11 0 121 2 -2 28 2010 1.08 12 12.96 144 1.05 0.02 29 2011 0 13 0 169 0.09 -0.09 30 2012 0 14 0 196 -0.86 0.86 31 2013 0 15 0 225 -1.82 1.82 32 2014 0 16 0 256 -2.78 2.78 Sum 386.68 16 -2424 2736 386.68

The Trend equation U = a + bt has been derived as

Inflow = 12.56 + -0.96 x Time

Where a = 12.56 and b = -0.96 (Fig. 5.4)

Fig. 5.4 Time series inflow of Ramgarh dam

Exceptional periods are (possible critical years) marked as *** where there is a

drastic change. In this analysis year 1984,1992, 1995, 1996, 1998, and 2005 are

identified as possible critical years It can be attributed to climatic or anthropogenic

changes.

5.3.2 Determination of Critical Year: Sequential Cluster Analysis

The final critical year can be obtained by applying Sequential Cluster Analysis on the

possible critical years which have been identified in Table 5.3. The minimum sum of

squared deviations for the hydrological series before and after the possible critical years

gives the unique critical year (Table 5.4).

y = -0.9594x + 12.563

-10.00

0.00

10.00

20.00

30.00

40.00

50.00

60.00

-20.00 -15.00 -10.00 -5.00 0.00 5.00 10.00 15.00 20.00

Infl

ow

(M

CM

)

Time series of inflow (Ramgarh)

Time series Ramgarh

Linear (Time series Ramgarh)


124

Table 5.4. Sequential cluster analysis: Ramgarh dam

S. N. Year Actual Inflow

(MCM)

Possible Critical Year

1984 1992 1995 1996 1998 2005

1 1983 55.45 553.7 1359.5 1273.2 1109.0 1118.3 1508.7

2 1984 8.38 554.0 104.0 129.7 189.5 185.7 67.7

3 1985 40.47 882.7 479.2 428.5 335.6 340.8 569.3

4 1986 20.56 96.1 3.9 0.6 2.5 2.1 15.6

5 1987 4.56 38.4 196.6 231.4 309.4 304.5 145.2

6 1988 20.25 90.0 2.8 0.2 3.6 3.1 13.2

7 1989 0.91 97.1 312.4 355.8 451.3 445.4 246.6

8 1990 1.39 87.8 295.6 337.9 431.1 425.3 231.7

9 1991 4.79 35.7 190.3 224.5 301.5 296.7 139.8

10 1992 29.00 332.7 108.6 85.2 46.9 48.9 153.5

11 1993 15.72 24.6 43.4 16.4 41.4 39.6 0.8

12 1994 5.89 23.7 10.5 192.6 264.4 259.8 114.9

13 1995 49.67 1514.3 1643.8 894.3 757.6 765.3 1093.2

14 1996 53.10 1792.8 1933.5 2141.9 958.0 966.7 1331.6

15 1997 27.78 289.8 347.9 439.4 553.8 33.3 124.8

16 1998 14.22 12.0 25.9 54.7 99.3 60.7 5.7

17 1999 8.07 7.2 1.1 1.6 14.6 34.9 72.9

18 2000 3.54 52.1 31.2 10.8 0.5 1.9 170.8

19 2001 1.93 78.0 51.9 24.0 5.4 0.1 215.6

20 2002 0.06 114.6 82.3 45.7 17.6 4.4 274.0

21 2003 7.34 11.7 3.2 0.3 9.5 26.8 86.0

22 2004 1.73 81.6 54.8 25.9 6.4 0.2 221.5

23 2005 7.31 11.9 3.3 0.2 9.3 26.5 86.6

24 2006 1.87 79.0 52.7 24.5 5.7 0.1 1.8

25 2007 0.85 98.2 68.6 35.6 11.6 1.7 0.1

26 2008 0.76 99.9 70.0 36.7 12.1 1.9 0.1

27 2009 0.00 115.8 83.4 46.5 18.1 4.7 0.3

28 2010 1.08 93.8 64.9 33.0 10.1 1.2 0.3

29 2011 0.00 115.8 83.4 46.5 18.1 4.7 0.3

30 2012 0.00 115.8 83.4 46.5 18.1 4.7 0.3

31 2013 0.00 115.8 83.4 46.5 18.1 4.7 0.3

32 2014 0.00 115.8 83.4 46.5 18.1 4.7 0.3

Mean On= 31.92 18.58 19.77 22.15 22.01 16.61

Mean ON-n= 10.76 09.13 06.82 04.25 2.16 0.51

Sum Vn+ VN-n= 7732 7959 7277 6048 5419 6894


125

From section 5.1 it is evident that there is a declining trend of water inflow to the

Ramgarh dam. It is very essential to determine the critical year, which divides two

consecutive non-overlapping epochs- pre-disturbance and post-disturbance.

Determination of critical year will help in policy formulation for an effective water

resources planning and management of the area. The year 1998 is considered as the

critical year for the hydrological behavior of the Ramgarh dam. This shows that some

disturbances have taken place after 1998 which is attributed to the low inflow to the dam.


The declining trend of inflow to the Ramgarh dam has been tested. The year of initiation

of post-disturbance epoch is determined as 1998. The various factors which are

considered responsible for surface runoff are analyzed and discussed. Trends of various

factors are plotted and shown in Fig 5.5 to 5.8. Climatic parameters such as average daily

minimum and maximum temperature, precipitation, wind speed, relative humidity and

solar radiation are studied, and graphs are plotted to determine the variation of these

parameters with time.

5.4.1 Rainfall and Other Climatic Factors

Year wise rainfall has been computed using the Thiessen polygon technique and the

graph has been plotted (Fig. 5.5 a). Variation of rainy days during monsoon period has

been shown in Fig. 5.5 (b). Minimum, maximum and average of rainfall of four rain

gauge stations within/around the Ramgarh catchment during the analysis period is shown

in Fig. 5.5 (c).


y = 6.3805x - 12200

0

200

400

600

800

1000

1980 1985 1990 1995 2000 2005 2010 2015 2020

Rai

nfa

ll (m

m)

Rainfall trend: Ramgarh


126

(b) Rainy days trend (1983-2014)

(c) Rainfall pattern over different RGS

Fig. 5.5 Rainfall trend: Ramgarh

The rainfall trend over the catchment area of Ramgarh dam varies significantly

over the years, showing the temporal variation of rainfall. But it does not vary

significantly within various RGS. To test the variances along the time scale and along the

RGS, F-test is applied. Computed F value is 156.26 which is more than the critical value

of Fc=8.62 (32-1=31 and 4-1=3 degree of freedoms). This shows that there exist

significant variances.

Minimum and maximum weighted annual rainfall is 209.7 mm (1987) and 921.9

mm (2003) with an average value of 551 mm. This shows that there exists temporal

variation of rainfall over the catchment. The pattern of rainfall intensity over the

y = 0.0137x + 4.5392

0

10

20

30

40

50

60

70

1980 1985 1990 1995 2000 2005 2010 2015 2020

Rain

y d

ays

(nos.

)

Rainy days trends: Ramgarh

0

200

400

600

800

1000

1200

Min. Max. Avg.

Ra

infa

ll (

mm

)

Rainfall distribution: Ramgarh

Shahpura

Amer

J. Ramgarh

Virat Nagar

Wtd. RF


127

catchment can be seen from Table 5.5. There is no much variation of rainfall intensities

over the catchment.

Table 5.5 Number of events for different rainfall intensities at four RGS

Year

No. of days (>20

mm/day)

No. of days (>50

mm/day)

No. of days (>100

mm/day)

No. of days (>150

mm/day)

SH AM JR VN SH AM JR VN SH AM JR VN SH AM JR VN

1983 10 10 8 6 4 3 0 1 0 0 0 0

1984 7 6 2 1 0 0 0 0

1985 11 10 9 4 2 3 1 1 0 0 0 0

1986 6 4 6 1 2 0 0 0 0 0 0 0

1987 6 2 2 1 1 0 0 0 0 0 0 0

1988 8 9 10 0 0 4 0 0 1 0 0 0

1989 7 8 7 5 1 2 2 0 0 0 0 0

1990 13 7 6 1 0 0 0 0 0 0 0 0

1991 2 8 9 5 0 2 2 0 0 0 0 0 0 0 0 0

1992 6 12 12 10 3 5 4 3 0 1 0 1 0 0 0 1

1993 9 11 7 13 5 5 2 2 0 1 0 0 0 1 0 0

1994 7 14 7 8 1 3 1 1 0 0 0 0 0 0 0 0

1995 17 13 13 19 4 6 4 2 0 1 2 0 0 1 0 0

1996 10 16 17 13 4 5 7 4 1 0 2 1 0 0 0 0

1997 8 9 10 12 3 3 1 2 0 0 0 0 0 0 0 0

1998 7 11 12 10 2 5 2 2 0 0 0 0 0 0 0 0

1999 8 4 8 10 2 3 4 2 0 0 0 0 0 0 0 0

2000 8 3 8 9 0 0 2 1 0 0 0 0 0 0 0 0

2001 5 6 4 8 1 0 0 2 0 0 0 0 0 0 0 0

2002 4 3 6 3 2 1 0 0 0 0 0 0 0 0 0 0

2003 11 8 15 16 7 2 6 6 1 1 0 1 0 0 0 0

2004 11 10 12 4 1 2 4 1 0 0 1 0 0 0 0 0

2005 12 7 12 14 5 3 6 7 2 0 2 1 1 0 1 1

2006 4 3 6 6 2 1 3 2 1 0 1 0 1 0 0 0

2007 7 5 9 0 2 2 0 0 0 1 0 0 0 0 0 0

2008 13 6 15 12 4 1 7 4 1 0 3 1 0 0 0 0

2009 4 0 8 3 2 0 3 1 0 0 0 0 0 0 0 0

2010 12 9 14 13 5 2 3 4 1 1 1 1 1 0 0 0

2011 14 7 11 12 5 1 0 6 0 0 0 0 0 0 0 0

2012 4 9 6 7 2 1 2 2 2 1 1 1 0 0 0 0

Here, abbreviations SH refers to Shahpura RGS, AM refers to Amer RGS; JR refers to

Jamwa Ramgarh RGS, and VN refers to Virat Nagar RGS.

Four numbers RGS are situated nearby catchment area of Ramgarh dam.

Weighted monsoon rainfall has been computed using the Thiessen polygon method. The


128

pattern of distribution of rainfall intensity over the catchment has also been analyzed and

found no major variation over the years. During 2003 and 2005, even after fairly

widespread and heavy to very heavy rainfall events, the inflow to the dam was only

around 7.3 MCM, which is around 10% of the design capacity of the dam. During the

year 2010 and 2011, the rainfall pattern was same, but the inflow to the dam remained on

zero level. Changes in land use, construction of small structures like check dams; anicuts,

field bunding, etc. also cause a reduction in inflow to the dam (Technical Committee

report 2013, SRSAC report, Appendix- 10b). Therefore, we can say that even after

widespread and more than normal rainfall, the water inflow to the Ramgarh dam has

diminished to zero level. Trends of other climatic parameters are shown in Fig. 5.6.

(a) Mean daily max. temp. (b) Mean of daily min. temp.

(c) Wind speed (d) Relative Humidity

31

32

33

34

35

36

37

1980 1990 2000 2010

Tem

p. (0

C)

Mean of daily max. temp.

17

18

19

20

21

1980 1990 2000 2010

Tem

p. (0

C)

Mean of daily min. temp.

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

1980 1990 2000 2010

Win

d s

peed

(m

/sec)

Mean of daily Wind speed

trend

0.30

0.35

0.40

0.45

0.50

0.55

0.60

1980 1990 2000 2010

RH

(fr

acti

on

)

Mean of daily RH trend


129

(e) Solar radiation

Fig. 5.6 Trends of other climatic factors: Ramgarh


Due to anthropogenic reasons land use and land cover is also changing with time. Land

use has been divided in two category viz. land use having a contributing effect and

reducing effect. One of the major land use is increasing in the cultivated area. Land use

trend (Contributing area) and Cultivation area trend are shown in Fig. 5.7.

(a) Contributing Land use (c) Cultivation area

Fig. 5.7 Land use (Contributing effect) and cultivation area trend: Ramgarh

Various types of the land use trend are shown in Fig. 5.8. These figures show that land

use having reducing effect, area sown are on an increasing trend. Average land holding is

17.00

17.50

18.00

18.50

19.00

19.50

1980 1990 2000 2010S

ola

r r

ad

iati

on

(M

J/m

2)

Mean of daily solar radiation

trend

0

5

10

15

20

25

30

35

1980 1990 2000 2010

Area

(%

)

Land use trend

(Contributing effect)

0.00

20.00

40.00

60.00

80.00

100.00

1980 1990 2000 2010

Area

(%

)

Cultivation area trend

Net crop areaArea sown more than onceTotal crop area


130

decreasing, and a total number of fields are increasing. This type of pattern of land use

increases the surface roughness and decreases the runoff.

(a) Various land uses

(b) Comparison of Contributing and Reducing land use changes

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1980 1983 1985 2003 2005 2010

Various land uses (Ramgarh)


0

10

20

30

40

50

60

70

80

90

100

1980 1983 1985 2003 2005 2010

Area

(%

)

Contributing and reducing land uses (Ramgarh)

Contributing area Reducing areaLinear (Contributing area) Linear (Reducing area)


131

(‘c) Average land holding and total nos. of fields

Fig. 5.8 Land use changes: Ramgarh

Construction of small storage structures like check dams, anicuts, johads, earthen

bunds, field bundings etc. are also causing a reduction in inflow to the dam. As mentioned

in a report of a technical committee (2013), in Ramgarh catchment area 430 water bodies

with a total storage capacity of 12.62 MCM exist as detailed below:

4 minor irrigation projects with a capacity 2.37 MCM

322 village tanks with a capacity 8.26 MCM

104 anicuts with a capacity 1.99 MCM

Similarly, Watershed & Soil Conservation Department constructed 342 water

bodies consisting of anicuts, WHS, etc. storing 1.07 MCM of water. Besides this, contour

bunding has also been done in about 67.80 sqkm with an average height of 0.3m. This

contour bunding stores about 1.52 MCM of water in a single spell of rainfall. So activities

of Watershed & Soil Conservation Department store about 2.59 MCM of runoff. So the

total interception in the yield is about 15.21 MCM. These types of activities intercept the

catchment area of Ramgarh dam and also increases the surface roughness, thereby causes

a reduction in inflow to the dam.

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

2.00

2.50

3.00

3.50

4.00

4.50

1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

Nu

mb

er o

f fi

eld

s (0

00)

Average l

an

d h

old

ing (

ha)

Average land holding and total nos. of fields (Ramgarh)

Average field size Number of fields


132

A field visit was also conducted on 29.01.2016 to 02.02.2016 to identify

abstractions in the catchment area of existing dams of capacity 45.0 mcft and found 104

number of such small storage structures of capacity 45.203 mcft (Appendix 10b), which

also have resulted in the nil inflow to the dams. Literature regarding the effect of this type

of land use change and an interception on inflow has been discussed in detail in section

2.6 and 2.11.3.


Net area sown within the catchment area of Ramgarh dam has increased from 50.66% to

62.75% from the year 1983 to 2014 and area sown more than once has increased from

19.22% to 35.43% during this period. A major part of this cultivation is done through

tube wells. Total annual draft within the catchment is 97.91 MCM through groundwater

pumping (Technical Committee report 2013) which is more than the capacity of Ramgarh

dam and also more than the replenishable ground water recharge. Gross ground water

draft for only irrigation in Jaipur is to the tune of 1081.11 MCM against the total annual

replenishable ground water recharge of 712.38 MCM (CGWB 2014). This infers that the

increasing cultivation area is lowering the groundwater Table. Year wise changes in pre-

monsoon and post-monsoon groundwater level below ground level in four blocks of

Ramgarh dam catchment along with the mean groundwater level are shown in Fig. 5.9.

(a) GWL below GL: Amer

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

19

82

1984

19

86

1988

19

90

1992

19

94

1996

19

98

2000

20

02

2004

20

06

2008

20

10

2012

20

14

2016

GW

L b

elo

w G

L(m

)

Temporal variation of GWL below GL: Amer

Pre Monsoon

Post Monsoon

Linear (Pre Monsoon)


133

(b) GWL below GL: Shahpura

(c) GWL below GL: Jamwa Ramgarh

(d) GWL below GL: Virat Nagar

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

GW

L b

elo

w G

L(m

)

Temporal variation of GWL below GL: Shahpura

Pre MonsoonPost MonsoonLinear (Pre Monsoon)

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

19

82

19

84

19

86

19

88

1990

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

2012

20

14

20

16

GW

L b

elo

w G

L(m

)

Temporal variation of GWL below GL: Jamwa Ramgarh

Pre MonsoonPost MonsoonLinear (Pre Monsoon)

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

19

82

1984

19

86

19

88

1990

19

92

19

94

19

96

19

98

20

00

20

02

20

04

2006

20

08

20

10

2012

20

14

20

16

GW

L b

elo

w G

L(m

)

Temporal variation of GWL below GL: Virat Nagar

Pre Monsoon

Post Monsoon

Linear (Pre Monsoon)


134

(e) Pre-monsoon and Post-monsoon GWL below GL: Ramgarh

Fig. 5.9 Pre-monsoon and Post-monsoon GWL below GL: Ramgarh

Pre-monsoon GWL of the catchment area of Ramgarh dam has decreased from

average level of 16.0m to 43.0m during the observation period, which depicts a lowering

trend of GWL at the rate of 82 cm per year. The rise of underground water level before

and after the monsoon period ranges from approximately 0.0m to 2.0m, with an average

rise of 0.36m. Development of lift facilities through pump sets has resulted in a

tremendous increase in irrigation by underground water using wells and tube wells.

Overexploitation of underground water has resulted in no block in the safe category in the

year 2014. Rivers are around 10-20m deep in different stretches. Lowering of

groundwater level below ground level is leading to the diminishing subsurface flow and

decreasing contribution to the surface runoff.


Annual monsoon rainfall, rainy days, land use, cultivation area and climatic parameters,

GWL below GL, which influence the runoff, have been plotted to show the trend over

time. Land use and cultivation area are influenced by the population growth, which is

plotted in the graph and shown in Fig.5.10.

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

GW

L b

elo

w G

L(m

)

Temporal variation of GWL below GL: Ramgarh Dam

Catchment

Wt. Ramgarh Dam Catchment

Linear (Wt. Ramgarh Dam Catchment)


135

(a) Population density: Ramgarh and Rajasthan

Fig. 5.10 Population density and its growth: Ramgarh

Population growth of Jaipur district is much more than the state. Population

density of the Ramgarh dam catchment may be considered as that of Jaipur city as

catchment area being near to the city area.

5.4.5 Various Parameters, Trend Lines and their Rate of Changes

Trend lines plotted in section 5.4 give an idea about the various factors, whether they are

on increasing or decreasing trend and at what rate. The results are summarized in Table

5.6.

0

100

200

300

400

500

600

700

800

1980

1985

1990

1995

2000

2005

2010

2015

2020

2025

Pop

ula

tion

/sq

km

Population density: Ramgarh

Population density: RamgarhPopulation density RajasthanPoly. (Population density: Ramgarh)Poly. (Population density Rajasthan)


136

Table 5.6 Factors and their trends: Ramgarh



4 Rainy days in a year (nos.) Increasing 1 day in 77 yrs.


6 Mean of daily min. temp. (0C) Decreasing 1 deg. in 77 yrs.



9 Mean of daily solar radiation (MJ/m2) Constant NA

10 Land use (Contributing effect) (%) Decreasing 1% in 2.5 yrs.

11 Cultivation area (%) Increasing 3.69% in 2 yrs.

12 Groundwater level below GL(m) Increasing 0.82m in 1 yrs.

13 Population density Increasing 720 in 2021

14 State population density Increasing 254 in 2021

Rainfall and rainy days trends over the catchment area of Ramgarh dam are increasing.

Groundwater level below ground level and population density are on steep increasing

trend. Land use having contributing effect is on decreasing trend.










137

Correlations

1.000 -.541** -.889** .997** .923** .303 -.012 .157 .359* -.256 -.017 -.004 .384* .619**

. .001 .000 .000 .000 .092 .948 .389 .043 .158 .927 .981 .030 .000

32 32 32 32 32 32 32 32 32 32 32 32 32 32

-.541** 1.000 .508** -.536** -.465** .327 .375* -.461** -.178 -.121 .471** -.166 -.075 -.350*

.001 . .003 .002 .007 .067 .035 .008 .330 .509 .006 .364 .683 .049

32 32 32 32 32 32 32 32 32 32 32 32 32 32

-.889** .508** 1.000 -.906** -.954** -.257 .019 .011 -.373* .132 -.061 .082 -.433* -.800**

.000 .003 . .000 .000 .156 .918 .951 .035 .471 .738 .655 .013 .000

32 32 32 32 32 32 32 32 32 32 32 32 32 32

.997** -.536** -.906** 1.000 .929** .303 -.024 .149 .351* -.249 -.020 .006 .352* .656**

.000 .002 .000 . .000 .092 .897 .415 .049 .169 .915 .972 .048 .000

32 32 32 32 32 32 32 32 32 32 32 32 32 32

.923** -.465** -.954** .929** 1.000 .320 .027 -.038 .344 -.203 .089 -.047 .496** .746**

.000 .007 .000 .000 . .074 .884 .838 .054 .265 .628 .797 .004 .000

32 32 32 32 32 32 32 32 32 32 32 32 32 32

.303 .327 -.257 .303 .320 1.000 .685** -.463** .189 -.409* .575** -.060 .056 .013

.092 .067 .156 .092 .074 . .000 .008 .299 .020 .001 .743 .759 .944

32 32 32 32 32 32 32 32 32 32 32 32 32 32

-.012 .375* .019 -.024 .027 .685** 1.000 -.490** .245 -.325 .748** -.164 .078 -.165

.948 .035 .918 .897 .884 .000 . .004 .176 .069 .000 .371 .672 .367

32 32 32 32 32 32 32 32 32 32 32 32 32 32

.157 -.461** .011 .149 -.038 -.463** -.490** 1.000 .138 .289 -.856** .531** -.092 .060

.389 .008 .951 .415 .838 .008 .004 . .453 .108 .000 .002 .615 .744

32 32 32 32 32 32 32 32 32 32 32 32 32 32

.359* -.178 -.373* .351* .344 .189 .245 .138 1.000 -.061 .156 -.132 .340 .195

.043 .330 .035 .049 .054 .299 .176 .453 . .741 .393 .472 .057 .286

32 32 32 32 32 32 32 32 32 32 32 32 32 32

-.256 -.121 .132 -.249 -.203 -.409* -.325 .289 -.061 1.000 -.440* .278 .131 .210

.158 .509 .471 .169 .265 .020 .069 .108 .741 . .012 .123 .473 .248

32 32 32 32 32 32 32 32 32 32 32 32 32 32

-.017 .471** -.061 -.020 .089 .575** .748** -.856** .156 -.440* 1.000 -.513** .160 -.098

.927 .006 .738 .915 .628 .001 .000 .000 .393 .012 . .003 .382 .592

32 32 32 32 32 32 32 32 32 32 32 32 32 32

-.004 -.166 .082 .006 -.047 -.060 -.164 .531** -.132 .278 -.513** 1.000 -.303 .076

.981 .364 .655 .972 .797 .743 .371 .002 .472 .123 .003 . .092 .679

32 32 32 32 32 32 32 32 32 32 32 32 32 32

.384* -.075 -.433* .352* .496** .056 .078 -.092 .340 .131 .160 -.303 1.000 .462**

.030 .683 .013 .048 .004 .759 .672 .615 .057 .473 .382 .092 . .008

32 32 32 32 32 32 32 32 32 32 32 32 32 32

.619** -.350* -.800** .656** .746** .013 -.165 .060 .195 .210 -.098 .076 .462** 1.000

.000 .049 .000 .000 .000 .944 .367 .744 .286 .248 .592 .679 .008 .

32 32 32 32 32 32 32 32 32 32 32 32 32 32

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

YEAR

INFLOW

LULC

POPDEN

WTRTBL

RAINFALL

RDAYS

MAXTMP

MINTMP

WINDSPD

RELHUM

SOLAR

NA

ASMO




Table 5.7 Correlation Matrix: Ramgarh


138


In section 5.4 trend lines and directions of various parameters which influence runoff

have been shown. Now it is essential to determine the principal components out of the

various components discussed earlier so that the same may be considered for further

planning and analysis.


Regression lines of various parameters (predictors) with inflow (dependent variable) have

been plotted as shown in Fig. 5.11. Coefficients of determinations have also been

mentioned on graphs. By coefficient of correlations and corresponding t-values compared

with critical t-values at 5% and 1% level of significance, we can determine the

significance level of the parameters for inflow to the dam.


(c) Land use (d) GWL below GL

y = 0.0276x - 3.1115

R² = 0.1072

0

10

20

30

40

50

60

0 200 400 600 800 1000

Infl

ow

(M

CM

)

Rainfall (mm)

Rainfall-Inflow

y = 0.4094x - 0.9488

R² = 0.1321

0

10

20

30

40

50

60

0 20 40 60 80

Infl

ow

(M

CM

)

Rainy days (nos.)

Rainy days-Inflow

y = 1.8464x - 31.932

R² = 0.2582

0

10

20

30

40

50

60

0 10 20 30 40

Infl

ow

(M

CM

)

Contributing land use (%)

Land use-Inflow

y = -0.9425x + 36.71 R² = 0.2159

0

10

20

30

40

50

60

0 20 40 60

Infl

ow

(M

CM

)

Water level (m)

GWL below GL-Inflow


139

(e) Mean daily max. Temp. (f) Mean daily min. temp.


(i) Solar radiation (j) Population density

Fig. 5.11 Cross plots: Between inflow and independent variables: Ramgarh

y = -7.7593x + 272.43

R² = 0.2137

0

10

20

30

40

50

60

30.00 32.00 34.00 36.00 38.00

Infl

ow

(M

CM

)


Mean daily max. temp.-Inflow

y = -4.5901x + 92.214 R² = 0.0314

0

10

20

30

40

50

60

16 17 18 19

Infl

ow

(M

CM

)



y = -19.867x + 64.133

R² = 0.0147

0

10

20

30

40

50

60

2.4 2.6 2.8 3.0

Infl

ow

(M

CM

)

Wind speed (m/sec)

Wind speed-Inflow

y = 231.34x - 79.672

R² = 0.2088

0

10

20

30

40

50

60

0.30 0.35 0.40 0.45 0.50

Infl

ow

(M

CM

)

Relative humidity


y = -5.8059x + 119.65

R² = 0.0282

0

10

20

30

40

50

60

17.00 18.00 19.00 20.00

Infl

ow

(M

CM

)

Mean of solar radiation (MJ/m2)

Mean daily solar radiation-

Inflow

y = -0.0899x + 45.439

R² = 0.2877

0

10

20

30

40

50

60

0 200 400 600

Infl

ow

(M

CM

)

Population density



140

Regression analysis of inflow with various factors independently, as shown in Fig.

5.11 is summarized in Table 5.8. Several cross plots showing the relationship between

independent variables and inflow may be generated to develop empirical relations for the

indirect estimation of the output. These relations are obtained using statistical methods

such as regression analysis. However, although empirical equations are cost- effective

and not too time-consuming, they include uncertainties relating to the limited data

available (Majdi et al. 2010). To determine the parameters affecting inflow, several cross

plots showing the relationship between independent variables and inflow have been

generated from a total of 32 data (Fig. 5.11 and Table 5.8).

Table 5.8 Correlation and regression of inflow with various parameters: Ramgarh

S.N. Factor

Regression

equation

Type of

correlation

Coefficient of

correlation

(R)

t-

value Significance

1 Rainfall y = 0.027x -3.111 Positive 0.30 1.72 NS

2 Rainy days y = 0.409x -0.948 Positive 0.36 2.14 S

3 Land use (Cont. effect) y = 1.846x -31.93 Positive 0.51 3.23 HS

4 GWL below GL y = -0.942x + 36.31 Negative 0.46 2.87 HS

5 Population density y = -0.089x + 45.43 Negative 0.54 3.48 HS

6 Mean of daily max. temp.

(0C)

y = -7.759x + 272.4 Negative 0.46 2.86 HS

7 Mean of daily min. temp. (0C) y = -4.59x + 92.21 Negative 0.18 0.99 NS

8 Mean of daily wind speed

(m/sec) y = -19.86x + 64.13 Negative 0.12 0.67 NS

9 Mean of daily RH (fraction) y = 231.3x –79.67 Positive 0.46 2.81 HS


(MJ/m2) y = -5.805x + 119.6 Negative 0.17 0.93 NS


The critical value of t for n=32-2=30 degree of freedom at 5% level of

significance is 2.042 and 1% level of significance is 2.75. Rainfall and rainy days are

having a weak correlation with the inflow, mean of daily max. temp. and mean of daily

relative humidity has moderate correlations and highly significant while other climatic

factors like mean daily min. temp., wind speed, and solar radiation are having no

correlation with inflow. Principal components derived out are population density, land

use, GWL, and mean of daily max. temp.. These are having a moderate correlation with


141

inflow and t-values are higher than the critical tc value. This concludes that population

density, land use, GWL, mean of daily max. temp., and mean of daily relative humidity

are principal components for the inflow to the Ramgarh dam. The effect of land use

changes, increase in agricultural land irrigation, mining activities and uncontrollable

water withdrawal on the hydrological regime of small rivers is also well explained by

Danil’Yan et al. (2006).



components and their relative influence so that the MLR can be further simplified as per

availability of data. Rainfall is also included in CAM being an important parameter for

the runoff. By relative influence value (Rij) computed by CAM (Annexure 8b), the

relative importance of the five factors is mentioned as below and shown in Fig. 5.12.

(1) LULC (Contributing area %)= 0.71

(2) Rainfall= 0.65

(3) Mean of daily max. temp.= 0.39

(4) GWL below GL= 0.29

(5) Population density= 0.25

Fig. 5.12 Relative influences of the factors responsible for inflow: CAM for Ramgarh

From the analyses, it is found that LULC, rainfall, mean daily max. temp., GWL,

and population density are the principal components for inflow to the Ramgarh dam.

0.71 0.65

0.39

0.29 0.25

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

LULC (Cont.area %)

Rainfall mean daily maxtemp.

GWL Populationdensity

Rel

ativ

e in

flu

ence

Factor

Relative influence (CAM): Ramgarh


142

Sensitivity analysis shows less water inflow to the Ramgarh dam due to the LULC is the

main factor having 31% of the effect; rainfall is responsible for 28% of the effect, mean

of daily max. temp. is having 22% of the effect, GWL below GL is having 13% of the

effect and population density has 11% effect on the inflow. Rainfall is not determined as

significant component as per regression analysis conducted independently while it is

determined as principal component influencing next to the LULC.


The inflow equation for Ramgarh dam may be formed with the help of Multiple Linear

Regression (MLR) considering the six principal components as derived in Section 5.4 and

5.5. The summary output of MLR is shown in Table 5.9.

Table 5.9 MLR summary output: Inflow to the Ramgarh dam

Regression Statistics Multiple R 0.77 R Square 0.60 Adjusted R Square 0.50 Standard Error 11.78 Observations 32.00

ANOVA df SS MS F Sig. F

Regression 6 5104.09 850.68 6.13 0.00 Residual 25 3467.58 138.70

Total 31 8571.68

Coefficients Standard

Error t Stat

Lower 95%

Upper 95%

Intercept -77.86 207.22 -0.38

-504.64 348.92

Rainfall 0.03 0.01 2.10

0.00 0.06

LULC (Cont. effect %) 1.22 1.60 0.76

-2.08 4.52

GWL (water Table) 0.46 1.08 0.42

-1.77 2.69 Mean of daily max. temp. 0.38 4.80 0.08

-9.49 10.26

Relative Humidity 135.52 136.53 0.99

-145.68 416.72

Population density -0.09 0.07 -1.26

-0.25 0.06

Using the computed coefficients, inflow equation is developed as shown below:

6655443322110 ****** xxxxxxInflow (5.1)


143

Where values of the coefficients are -77.86, 0.03, 1.22, 0.46, 0.38, 135.52, -0.09

and predictors are rainfall, LULC (Contributing effect), GWL below GL, Mean of daily

max. temp, relative humidity, and population density respectively, and a minimum value

of computed inflow is set to zero. The correlation coefficient (multiple R) for the above

relationship is 0.77.

From the developed equation, the inflow has been computed, and a graph is

plotted between inflow and computed inflow along with the goodness of fit as shown in

Fig.5.13. Between the two values coefficient of correlation (R) is 0.80 and the

corresponding t-value is 7.27, which is more than the critical value at 1% level of

significance, which infers that the inflow values computed from the developed equation

are highly significant.

Fig. 5.13 Goodness of fit for the developed equation (MLR): Ramgarh

5.7 Factors Responsible for Principal Components

As far as changes in land use are concerned, agricultural area (Total crop area) is

increasing with time and population. It has increased from 563 sqkm to 819 sqkm from

1970 to 2012. The area sown more than once is also increasing; it has increased from

around 70 sqkm to 311 sqkm. Effect of land use changes in absorption of most intense

storms was also inferred by Hakkim et al. (2014) for Western Ghats of Kerala. Due to a

tremendous increase in agricultural areas, land use pattern has changed at a very fast pace.

The increase in groundwater irrigation facilities resulted in lowering of the water Table

and increase in dark zones within the catchment area of Ramgarh dam. Agricultural

y = 0.5499x + 6.4753 R² = 0.6388

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

0.00 10.00 20.00 30.00 40.00 50.00 60.00

Co

mp

ute

d In

flo

w (

MLR

)

Observed Inflow

Goodness of fit for developed equation (MLR): Ramgarh


144

activities, land area sown, forest area, land area put to nonagricultural uses are increasing

at a very fast pace. Land utilization of the state is plotted in graphs as shown in Fig. 5.7

and 5.8 show that land utilization which is having a reducing effect on the surface runoff

viz. agricultural, forest and infrastructural uses are increasing and the land utilization

which is having a contributing effect on the surface runoff viz. barren land, culturable

waste, and fallow land are decreasing with the passage of time. These types of land use

changes, increase the surface roughness, are not favorable to the surface runoff. Due to

continuous partitions of the fields, the average size of the field is decreasing, and the

number of total fields is increasing with time. The average size of the field has decreased

from 3.97 ha to 2.4 ha from 1970 to 2015. Increased agricultural plough activities before

the rainy season and decreased average field sizes increase the infiltration rates with the

potential to absorb even most intense storms, results in very less surface runoff, making

excess infiltration flow or Hortonian Overland Flow a rare phenomenon in the observed

watersheds. Groundwater Table (average 44.5 m below the ground surface) of the area

has declined far below the level of the coarse-grained soil. Therefore, neither it is

contributing effectively to the groundwater, nor the subsurface flow.


145


BISALPUR DAM





Series Analysis


Cluster Analysis







of Changes









146

CHAPTER-6

TEMPORAL VARIATION OF INFLOW TO THE BISALPUR DAM

Year wise inflow data of Bisalpur dam have been shown in Table 6.1. Temporal variation

of inflow to the Bisalpur dam has been shown in Fig. 6.1 It is evident that the water

inflow trend to the dam is declining. The average inflow on the basis of last 35 years data

series (1979-2013), 18 years data series (1996-2013), and 13 year data series (2001-2013)

is 1170 MCM, 684 MCM, and 609 MCM against the design capacity of 1095 MCM.

During last 35 year, Bisalpur Dam received a minimum inflow of 17 MCM in the year

2002. The dam received 13 times water equal to or more than its design capacity,

therefore the performance dependability of the dam may be considered as 36.45%.

However, if we compute the yield at different dependability’s, the dam yields only 804

MCM, 314 MCM, and 122 MCM at 50%, 75%, and 90% dependability’s respectively.

Theoretical yield has been computed using Strange’s Table considering the catchment as

‘average’. Theoretical yield, actual inflow and weighted rainfall have been plotted against

the successive years.

Fig. 6.1 Temporal variations of rainfall, theoretical yield, and observed inflow to the

Bisalpur dam

0

100

200

300

400

500

600

700

800

900

0

1000

2000

3000

4000

5000

6000

1975 1980 1985 1990 1995 2000 2005 2010 2015

Rain

fall

(m

m)

Infl

ow

(M

CM

)

Rainfall, theoretical inflow and observed inflow trend: Bisalpur

Theoretical yield Observed inflow Rainfall

Linear (Theoretical yield) Linear (Observed inflow) Linear (Rainfall)


147

Table 6.1 Year wise inflow to the Bisalpur dam

S.N

.

Yea

r

Theo

reti

cal

Yie

ld

(MC

M)

Obse

rved

Infl

ow

(MC

M)

35 yr data series

(1979-2013)

18 Yr data series

(1996-2013)

13 Yr data series

(2001-2013)

Inflow in

Descending order

(MCM)

Dn

Inflow in

Descending order

(MCM)

Dn

Inflow in

Descending order

(MCM)

Dn

1 1979 1490 1234 4915 2.8

2 1980 245 712 3592 5.6

3 1981 339 477 3122 8.3

4 1982 839 3592 2464 11.1

5 1983 1504 2334 2334 13.9

6 1984 572 2464 2183 16.7

7 1985 295 1076 2071 19.4

8 1986 624 4915 2012 22.2

9 1987 246 1542 1952 25.0

10 1988 770 661 1767 27.8

11 1989 997 1376 1542 30.6

12 1990 1492 2183 1376 33.3

13 1991 742 1952 1234 36.1

14 1992 1186 791 1076 38.9

15 1993 376 811 1035 41.7

16 1994 1779 2071 811 44.4

17 1995 678 443 804 47.2

18 1996 1767 3122 804 50.0 3122 5.26

19 1997 883 324 791 52.8 2012 10.53

20 1998 358 90 712 55.6 1767 15.79

21 1999 617 537 661 58.3 1035 21.05

22 2000 313 314 576 61.1 804 26.32

23 2001 963 1035 537 63.9 804 31.58 2012 7.14


148

Rainfall-Inflow trends for different data series are shown in Fig. 6.2 (a). Inflow trend at different dependability’s for 35, 18,

and 13-year data series is shown in Fig. 6.2 (b). Inflow is showing a declining trend for all three dependability’s with longer

to shorter data series.

24 2002 195 17 477 66.7 576 36.84 1767 14.29

25 2003 741 177 443 69.4 537 42.11 1035 21.43

26 2004 1272 1767 324 72.2 324 47.37 804 28.57

27 2005 948 169 314 75.0 314 52.63 804 35.71

28 2006 1683 2012 235 77.8 235 57.89 576 42.86

29 2007 600 143 177 80.6 177 63.16 235 50.00

30 2008 778 161 169 83.3 169 68.42 177 57.14

31 2009 328 21 161 86.1 161 73.68 169 64.29

32 2010 950 235 143 88.9 143 78.95 161 71.43

33 2011 1244 804 90 91.7 90 84.21 143 78.57

34 2012 1224 576 21 94.4 21 89.47 21 85.71

35 2013 1465 804 17 97.2 17 94.74 17 92.86

Inflow Average =

1170.00 MCM 684.00 MCM 609.00 MCM

Inflow at 50% Dn =

803.88 MCM 318.89 MCM 234.67 MCM

Inflow at 75% Dn =

313.79 MCM 156.75 MCM 152.08 MCM

Inflow at 90% Dn =

121.67 MCM 20.81 MCM 18.69 MCM


149

Graph of three-time segments 1979-2013, 1996-2013 and 2001-2013 (Fig. 6.2)

shows a reduction of inflow at the same quantum of monsoon rainfall. The figure can also

be used to predict the maximum probable inflow for a certain amount of monsoon rainfall

using trend line of 2001-2013.

(a) Rainfall-Inflow trend for different data series

(b) Inflow at different dependability for different data series

Fig. 6.2 Water Inflow trend for different time segment: Bisalpur


(1) Z Test for 35 years data series (1979-2013):

0

1000

2000

3000

4000

5000

6000

300 400 500 600 700 800 900

Infl

ow

(M

CM

)

Rainfall (mm)

Rainfall-Inflow trend for different data series: Bisalpur

Trend 1979-2013 Trend 1996-2013 Trend 2001-2013

Linear (Trend 1979-2013) Linear (Trend 1996-2013) Linear (Trend 2001-2013)

804

319

235

314

157 152 122

21 19

0

100

200

300

400

500

600

700

800

900

35 yr data series (1979-2013) 18 Yr data series (1996-2013) 13 Yr data series (2001-2013)

Infl

ow

(M

CM

)

Dependable inflow for different data series: Bisalpur

50% dependability 75% dependability 90% dependability


150

Ho (Null Hypothesis): 1095 MCM


For n=35 (>30), z-test has been applied.

1170x ; S= 1130.22; SE= 191.04;

Z= 0.39;

Critical z value at 5% significance level= 1.96 (two tail) and 1.645 (one tail)

Z<zc; therefore null hypothesis is accepted, i.e. Bisalpur dam is getting the desired 1095

MCM inflow of water.

(1I) T-Test for 18 years data series (1996-2013):



For n=18 (<30), t-test has been applied.

684x ; S= 835.33; SE= 196.89;

t= -2.09;

Critical t value at 5% significance level= 1.74 (one tail) at df= 18-1= 17

t>tc; therefore null hypothesis is rejected and alternate hypothesis is accepted, i.e.

Bisalpur dam is receiving less than the desired 1095 MCM inflow of water.

(1II) T-Test for 13 year data series (2001-2013):



For n=13 (<30), test has been applied.

609x ; S= 658.48; SE= 182.63;

t= -2.66;

Critical t value at 5% significance level= 1.782 (one tail) at df= 13-1= 12

t>tc; therefore null hypothesis is rejected and alternate hypothesis is accepted, i.e.

Bisalpur dam is receiving less than the desired 1095 MCM inflow of water.

Hypotheses tested on three data series shows that water inflow in Bisalpur dam is

on decreasing trend


151


By year wise inflow data (Table 6.1) hydrological performance of the dam regarding

reliability, resilience, vulnerability, maximum deficit and sustainability can be determined

for different data series, which is as shown below:

35 years data series

(1979-2013) 18 Year data series

(1996-2013) 13 Year data series

(2001-2013)

Reliability: 37.14% 16.67% 15.38%

Resilience:

31.82% 13.33% 18.18%

Vulnerability:

35.78% 55.90% 55.52%

Maximum Deficit(2002):

98.45% 98.45% 98.45%

Sustainability 42.34% 21.40% 23.17%

Inflow on dependability is plotted as shown in Fig. 6.3. The graph shows that

there was 17 MCM inflow at 97.20% reliability. As per code provisions, inflow at 100%

reliability is considered for drinking water projects (IS 5477 Part-3:2002). Danil’Yan et

al. (2006) recommends guarantee of 95% occurrence for water supply. The dam was

designed for 1095 MCM at 75% dependability, but it is yielding only 314 MCM as per 35

years data series. For 13 year or 18-year data series, it is yielding only half of this i.e.

around 152 MCM at 75 % dependability.

Fig. 6.3 Variations of inflow with dependability in Bisalpur Dam

Theoretical inflow computed by annual rainfall, and catchment area has been compared

with observed values. The performance of method of computing theoretical inflow is

shown in Table 6.2.

0

1000

2000

3000

4000

5000

6000

0.0 20.0 40.0 60.0 80.0 100.0 120.0

Infl

ow

(M

CM

)

Dependability (%)

Dependability-Inflow Curve

1979-2013 1996-2013 2001-2013


152

Table 6.2 Performance Statistics: Bisalpur dam

Statistical

Parameter MAE MAPE (%) R R2 t33 NSE RMSE RSR NMBE

Value 713.57 170.51 0.36 0.13 2.22 0.05 1085.38 0.97 -25.49

Optimal

value

Lower

value

Lower

value Unity Unity 2.034 Unity

Lower

value Zero

Lower

Value

The value of the coefficient of correlation is 0.36, the corresponding t-value is

2.22, which is more than the critical value of tc = 2.034, at (35-2) =33 degree of freedom

at 5% level of significance. This shows that there is a marginal correlation between the

observed and computed values. Nash-Sutcliff Efficiency (NSE) is a low positive value,

which also shows a marginal correlation. RSR is far beyond the optimal value. MAE,

MAPE, and NMBE are very high. Therefore, it can be concluded that the method of

computation of theoretical values of inflow is not an accepTable method in this case.


Performance dependability of Bisalpur dam is only 36.45%, and water inflow trend to the

Bisalpur dam is declining. Therefore, it is essential to find out the critical year. Time

Series Analysis and Sequential Cluster Analysis have been carried out for the purpose.

Time series analysis gives us an idea about the possible critical years where trends are

broken, and abrupt changes have taken place. The Sequential cluster analysis gives us the

critical year out of the possible critical years, which divides the pre and post-disturbance

epochs.


To determine the critical year using sequential cluster analysis, we need to determine

possible critical years. Possible critical years have been identified with the help of time

series analysis by trend elimination using least square method. The results of the time

series analysis are as shown in Table 6.3. Time series of inflow for Bisalpur dam has been

shown in Fig. 6.4.


153

Table 6.3. Time series analysis: Bisalpur Dam (Trend elimination using least square

method) S. N. Year

Inflow t t*I t*t Trend Elimination of

Trend

Possible

critical year

MCM

1 1979 1233.93 -17 -20977 289 2050 -816.18

2 1980 712.26 -16 -11396 256 1998 -1286.06

3 1981 476.64 -15 -7150 225 1947 -1469.9

4 1982 3592.47 -14 -50295 196 1895 1697.71 ***

5 1983 2333.9 -13 -30341 169 1843 490.92

6 1984 2464.46 -12 -29574 144 1791 673.27

7 1985 1075.62 -11 -11832 121 1739 -663.78

8 1986 4914.76 -10 -49148 100 1688 3227.14 ***

9 1987 1542.34 -9 -13881 81 1636 -93.49

10 1988 661.29 -8 -5290 64 1584 -922.75

11 1989 1375.81 -7 -9631 49 1532 -156.45

12 1990 2182.95 -6 -13098 36 1480 702.47

13 1991 1951.57 -5 -9758 25 1429 522.87

14 1992 791.28 -4 -3165 16 1377 -585.63

15 1993 810.82 -3 -2432 9 1325 -514.3

16 1994 2070.8 -2 -4142 4 1273 797.45

17 1995 442.93 -1 -442.9 1 1222 -778.62

18 1996 3122.34 0 0 0 1170 1952.56 ***

19 1997 323.99 1 323.99 1 1118 -793.99

20 1998 90.06 2 180.12 4 1066 -976.14

21 1999 537.24 3 1611.7 9 1014 -477.17

22 2000 313.79 4 1255.2 16 962.6 -648.84

23 2001 1034.55 5 5172.8 25 910.8 123.7

24 2002 16.99 6 101.94 36 859.1 -842.07

25 2003 176.72 7 1237 49 807.3 -630.55

26 2004 1767.49 8 14140 64 755.5 1011.99 ***

27 2005 168.51 9 1516.6 81 703.7 -535.19

28 2006 2011.89 10 20119 100 651.9 1359.96 ***

29 2007 142.74 11 1570.1 121 600.1 -457.39

30 2008 161.43 12 1937.2 144 548.4 -386.92

31 2009 21.23 13 275.99 169 496.6 -475.33

32 2010 234.67 14 3285.4 196 444.8 -210.11

33 2011 804.29 15 12064 225 393 411.29

34 2012 576.32 16 9221.1 256 341.2 235.1

35 2013 803.88 17 13666 289 289.4 514.45

Sum 40942 0 -184872 3570 40942

The Trend equation U = a + bt has been derived as

Inflow = (1169.77) + (-51.78) x Time

Where a = 1169.77 and b = -51.78


154

Fig. 6.4 Time series inflow of Bisalpur dam

Exceptional periods are (possible critical years) marked as *** where there are

changes that can be attributed to climatic or anthropogenic changes.

6.3.2 Determination of Critical Year: Sequential Cluster Analysis

A critical year for the possible critical years is determined using sequential cluster

analysis as shown in Table 6.4. From section 6.1 it is evident that there is a declining

trend of water inflow to the Bisalpur Dam. It is essential to determine the critical year,

which divides two consecutive non-overlapping epochs- pre-disturbance and post-

disturbance. Determination of critical year will help in policy formulation for an effective

water resources planning and management of the area. Sequential Cluster Analysis has

been employed to determine the critical year.

y = -51.785x + 1169.8

0

1000

2000

3000

4000

5000

6000

-20 -10 0 10 20

Time series of inflow (Bisalpur)

Time series Bisalpur

Linear (Time series Bisalpur)


155

Table 6.4. Sequential cluster analysis: Bisalpur Dam

S.

N. Year

Actual

Inflow

(MCM)

Possible Critical Year

1982 1986 1996 2001 2004 2006

1 1979 1234 72939 751814 280976 61045 22823 16919

2 1980 712 626848 1928591 1106151 590957 452575 424761

3 1981 477 1055478 2638560 1657307 1008748 825126 787416

4 1982 3592 4361693 2224473 3343291 4458292 4872909 4966064

5 1983 2334 1471126 54242 324786 727438 900411 940706

6 1984 2464 1804878 132101 490641 967189 1165229 1211007

7 1985 1076 2060 1051412 473873 164336 95718 83165

8 1986 4915 14392577 7917217 9927257 11790674 12459171 12607861

9 1987 1542 177527 420344 49133 3763 24756 31805

10 1988 661 211337 54156 1215979 671931 523762 493807

11 1989 1376 64930 232145 150688 11064 84 140

12 1990 2183 1127740 1661395 175520 492735 636726 670681

13 1991 1952 689850 1118458 35183 221438 321004 345241

14 1992 791 108717 10552 946190 475717 352507 328011

15 1993 811 96213 6919 908555 449143 329684 306010

16 1994 2071 902123 1384862 94127 347866 470324 499568

17 1995 443 459773 203461 1745215 1077581 887488 848363

18 1996 3122 4005382 4965521 1845101 2694013 3018367 3091777

19 1997 324 635229 324914 46661 1338678 1125747 1081626

20 1998 90 1062838 646320 202446 1934716 1676871 1622924

21 1999 537 340774 127277 8 890680 718694 683529

22 2000 314 651585 336641 51170 1362374 1147486 1102937

23 2001 1035 7473 19755 244581 199317 122814 108537

24 2002 17 1218833 769142 273537 310258 1871445 1814430

25 2003 177 891664 514490 131972 157831 1459939 1409633

26 2004 1767 417947 762981 1506727 1424414 146297 162803

27 2005 169 907242 526339 138007 164424 143257 1429202

28 2006 2012 793693 1249688 2166474 2067541 2145916 419767

29 2007 143 957001 564398 157819 185989 163430 62133

30 2008 161 920780 536663 143317 170216 148666 53164

31 2009 21 1209494 761727 269122 305555 276434 137470

32 2010 235 785581 434716 93226 115145 97550 24753

33 2011 804 100305 8048 69849 53033 66198 169983

34 2012 576 72939 751814 280976 61045 22823 16919

35 2013 804 626848 1928591 1106151 590957 452575 424761

Mean On= 1504 2101 1764 1481 1385 1364

Mean ON-n= 1121 894 540 574 547 392

Sum Vn+ VN-n= 42531629 34339324 30264890 36894099 38669410 37936191


156

The minimum sum of squared deviations of hydrological series before and after the

possible critical year is corresponding to the year 1996 as shown in Table 6.4. Therefore,

the year 1996 is considered as the critical year for the hydrological behavior of the

Bisalpur Dam. This shows that some disturbances have taken place after 1996 which is

attributed to the low inflow to the dam.


The declining trend of inflow to the Bisalpur dam has been observed. The critical year of

initiation of post-disturbance epoch is determined as 1996. The various factors which are

considered responsible for surface runoff are analyzed and discussed. Trends of various

factors are plotted and shown in Fig. 6.5 to 6.10. Climatic parameters such as average

daily minimum and maximum temperature, precipitation, wind speed, relative humidity

and solar radiation are studied, and graphs are plotted to determine the variation of these

parameters with time.

6.4.1 Rainfall and Other Climatic Factors

Eleven numbers RGS are situated within and nearby catchment area of Bisalpur dam.

Year wise rainfall has been computed using the Thiessen polygon technique and the

graph has been plotted (Fig. 6.5 a). Variation of rainy days during monsoon period has

been shown in Fig. 6.5 (b).


0

100

200

300

400

500

600

700

800

900

1975 1980 1985 1990 1995 2000 2005 2010 2015

Rain

fall

(m

m)

Rainfall trend-Bisalpur


157

(b) Rainy days trend (1979-2013)

Fig. 6.5 Rainfall trend: Bisalpur

The rainfall trend over the catchment area of Bisalpur Dam varies significantly

over the years, showing the temporal variation of rainfall and the trend of variation is

increasing. Minimum and maximum weighted annual rainfall is 336 mm (2002) and 806

mm (1994) with an average value of 573 mm. This shows that there exists temporal

variation of rainfall over the catchment. During 2005, 2010, and 2013, even after more

than average rainfall, the inflow to the dam was less than design capacity. Trends of other

climatic parameters are shown in Fig. 6.6.

(a) Mean of daily maximum and daily minimum temperature

0

10

20

30

40

50

60

1975 1980 1985 1990 1995 2000 2005 2010 2015

Rain

y d

ays

Rainy days trend-Bisalpur

17.00

18.00

19.00

20.00

21.00

22.00

23.00

24.00

25.00

30.00

31.00

32.00

33.00

34.00

35.00

36.00

1975 1980 1985 1990 1995 2000 2005 2010 2015

Min

. te

mp

. (0

C)

Max. te

mp

. (0

C)

Mean of daily max. and daily min. temperature: Bisalpur

Mean of daily max. temp. Mean of daily min. temp.Linear (Mean of daily max. temp.) Linear (Mean of daily min. temp.)


158

(b) Mean of daily wind speed

(c) Mean of daily relative humidity and solar radiation

Fig. 6.6 Trends of other climatic factors: Bisalpur

There is an increasing trend of mean of daily maximum and minimum

temperature, daily relative humidity and daily solar radiation while a slightly decreasing

trend of mean of daily wind speed. These trends are shown in Table 4.8.

2.00

2.20

2.40

2.60

2.80

3.00

3.20

3.40

1975 1980 1985 1990 1995 2000 2005 2010 2015

Win

d s

peed

(m

/s)

Mean of daily wind speed: Bisalpur

17.00

18.00

19.00

20.00

21.00

22.00

23.00

24.00

25.00

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

1975 1980 1985 1990 1995 2000 2005 2010 2015

Sola

r r

ad

iati

on

(M

J/m

^2

)

Rela

tive h

um

idit

y (

Fra

cti

on

) Mean of daily relative humidity and solar radiation: Bisalpur

Relative humidity Solar radiation

Linear (Relative humidity) Linear (Solar radiation)


159


Due to anthropogenic reasons, land use land cover is changing with time. Land use has

been divided in two category viz. land use having a contributing effect and reducing

effect. One of the major land use is increasing cultivated area. Land use trend

(Contributing area) is shown in Fig. 6.7. Various types of land use trends are shown in

Fig. 6.8. These figures show that land use having reducing effect viz. area sown, area

sown more than once are on an increasing trend.

Fig. 6.7 Land use (Contributing effect): Bisalpur

25.00

30.00

35.00

40.00

45.00

50.00

19

78

19

80

19

82

19

84

19

86

19

88

19

90

19

92

1994

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

20

12

La

nd

use

% (

Con

trib

uti

ng e

ffect)

LULC (Contributing effect): Bisalpur

0%

20%

40%

60%

80%

100%

1978

1980

1985

1992

1995

2000

2003

2007

2008

2009

2010

2012

Area

(%

)

Land use changes: Bisalpur dam catchment



160

Fig. 6.8 Land use changes: Bisalpur

0

10

20

30

40

50

60

70

80

1978 1980 1985 1992 1995 2000 2003 2007 2008 2009 2010 2012

Area

(%

)

Contributing and reducing areas (%): Bisalpur dam catchment

Contributing effect Reducing effectLinear (Contributing effect) Linear (Reducing effect)

0

10

20

30

40

50

60

70

1978 1980 1985 1992 1995 2000 2003 2007 2008 2009 2010 2012

Area

(%

)

Cultivation area: Bisalpur dam catchment

Area sown more than once Net area sown

2.00

2.05

2.10

2.15

2.20

2.25

2.30

2.35

2.40

2.45

2.50

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

Average f

ield

siz

e (

ha)

Average size of field: Bisalpur dam catchment


161

Agricultural area is increasing with time and population. It has increased from

5,691 sqkm to 6,916 sqkm from 1978 to 2012. The area sown more than once is also

increasing; it has increased from around 1,973 sqkm to 3,520 sqkm. Due to this increase

in agricultural areas, land use pattern changed at a very fast pace.

Due to continuous partitions of the fields, the average size of the field is

decreasing, and the total number of fields is increasing with time. The average size of the

land holding has decreased from 2.45 ha to 2.07 ha from 1980 to 2000. This reflects that

total number of fields are increasing because of partitions of large fields, thereby more

length of periphery boundaries increasing the surface roughness and decreasing the

resultant runoff to the reservoir.


Year wise changes in pre monsoon and post monsoon groundwater level below ground

level in the catchment area of Bisalpur dam are shown in Fig. 6.9. Average river bed level

below ground level within the catchment area was measured at some random places and

found in between 12-15m. After the year 1996 the pre-monsoon ground water level went

down the average river bed level, and post monsoon water levels were also deeper than

the average river bed level in some years. No recorded data is available to mention the

contribution of groundwater to rivers. The local enquiry was conducted by the old people

of the area, and they told that earlier to 1995-1996, some deep rivers of the catchment

flowed during the post-monsoon period also. However, according to the literature

reviewed earlier, the declining water table reduces runoff due to baseflow and hence the

inflow to the wetland (Jain et al. 2008).


162

Fig. 6.9 Pre-monsoon and Post-monsoon GWL below GL: Bisalpur

Pre-monsoon GWL below ground level of the catchment area of Bisalpur Dam

has increased from an average level of 10.0m to 17.0m during the observation period,

which depicts a lowering trend of GWL at the rate of 15 cm per year. The rise of

underground water level before and after the monsoon period ranges from approximately

0.5m to 8.0m, with an average rise of 4.9m. Development of lift facilities through pump

sets has resulted in a tremendous increase in irrigation from underground water using

wells and tube wells.


Land use and cultivation area are influenced by the population growth, which is plotted in

the graph and shown in Fig 6.10. As the population increases, cultivation and other

anthropogenic disturbances increases and fallow land decrease, therefore, reducing the

surface runoff.

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

GW

L(m

)

Temporal Variation of GWL below GL of Bisalpur Dam Catchment

Premonsoom GWL Postmonsson GWL Average river bed level


163

Fig. 6.10 Population density and its growth: Bisalpur

Population growth in the catchment area of Bisalpur dam has been derived by

applying weights to the corresponding districts density. Population density has an

excellent negative correlation with LULC (contributing effect) at 1% level of significance

(Table 6.6)

6.4.5 Various Parameters, Trend Lines and their Rate of Changes

Trend lines plotted in section 6.4 give an idea about the various factors, whether they are

in increasing or decreasing trend and at what rate. The results are summarized in Table

6.5.

Table 6.5 Factors and their trends: Bisalpur



2 Rainy days in a year (nos.) Increasing 1 day in 3 yrs.


4 Mean of daily min. temp. (0C) Increasing 1 deg. in 250 yrs.



7 Mean of daily solar radiation (MJ/m2) Decreasing 1MJ/m2 in yrs.

8 Land use (Contributing effect) (%) Decreasing 1% in 2.5 yrs.

9 Cultivation area (%) Increasing 1% in 2 yrs.

10 Groundwater level below GL(m) Increasing 0.15m in 1 yrs.

11 Population density Increasing 450 in 2021

12 State population density Increasing 254 in 2021

0

50

100

150

200

250

300

350

400

450

1975 1980 1985 1990 1995 2000 2005 2010 2015

Pop

ula

tion

den

sity

Population density: Bisalpur


164

Rainfall and rainy days trends over the catchment area of Bisalpur dam are

increasing. Groundwater levels below GL and population density are on increasing trend.

6.4.6 Correlation Matrix: Bisalpur








Table 6.6 Correlation Matrix: Bisalpur


165

Correlations

1.000 -.469** -.943** .998** .810** .195 .327 .100 .048 -.319 .142 .342* .816** .265

. .004 .000 .000 .000 .262 .055 .566 .783 .062 .417 .045 .000 .124

35 35 35 35 35 35 35 35 35 35 35 35 35 35

-.469** 1.000 .362* -.473** -.337* .345* -.147 -.270 -.300 -.059 .063 -.194 -.308 .024

.004 . .033 .004 .048 .042 .398 .116 .080 .738 .721 .264 .071 .893

35 35 35 35 35 35 35 35 35 35 35 35 35 35

-.943** .362* 1.000 -.938** -.669** -.282 -.305 .034 .017 .451** -.231 -.182 -.907** -.528**

.000 .033 . .000 .000 .100 .075 .847 .921 .007 .182 .296 .000 .001

35 35 35 35 35 35 35 35 35 35 35 35 35 35

.998** -.473** -.938** 1.000 .804** .192 .320 .097 .043 -.330 .148 .361* .803** .255

.000 .004 .000 . .000 .268 .061 .580 .808 .053 .397 .033 .000 .140

35 35 35 35 35 35 35 35 35 35 35 35 35 35

.810** -.337* -.669** .804** 1.000 .190 .306 .341* .187 -.132 -.092 .407* .575** -.056

.000 .048 .000 .000 . .275 .074 .045 .283 .449 .598 .015 .000 .750

35 35 35 35 35 35 35 35 35 35 35 35 35 35

.195 .345* -.282 .192 .190 1.000 .506** -.573** -.359* -.593** .560** -.281 .252 .273

.262 .042 .100 .268 .275 . .002 .000 .034 .000 .000 .102 .144 .112

35 35 35 35 35 35 35 35 35 35 35 35 35 35

.327 -.147 -.305 .320 .306 .506** 1.000 -.588** -.170 -.377* .750** -.278 .319 .144

.055 .398 .075 .061 .074 .002 . .000 .329 .025 .000 .106 .062 .409

35 35 35 35 35 35 35 35 35 35 35 35 35 35

.100 -.270 .034 .097 .341* -.573** -.588** 1.000 .626** .528** -.853** .450** -.077 -.386*

.566 .116 .847 .580 .045 .000 .000 . .000 .001 .000 .007 .661 .022

35 35 35 35 35 35 35 35 35 35 35 35 35 35

.048 -.300 .017 .043 .187 -.359* -.170 .626** 1.000 .418* -.223 -.093 .027 -.175

.783 .080 .921 .808 .283 .034 .329 .000 . .013 .197 .594 .878 .315

35 35 35 35 35 35 35 35 35 35 35 35 35 35

-.319 -.059 .451** -.330 -.132 -.593** -.377* .528** .418* 1.000 -.538** .054 -.370* -.428*

.062 .738 .007 .053 .449 .000 .025 .001 .013 . .001 .760 .029 .010

35 35 35 35 35 35 35 35 35 35 35 35 35 35

.142 .063 -.231 .148 -.092 .560** .750** -.853** -.223 -.538** 1.000 -.456** .253 .355*

.417 .721 .182 .397 .598 .000 .000 .000 .197 .001 . .006 .143 .036

35 35 35 35 35 35 35 35 35 35 35 35 35 35

.342* -.194 -.182 .361* .407* -.281 -.278 .450** -.093 .054 -.456** 1.000 .056 -.276

.045 .264 .296 .033 .015 .102 .106 .007 .594 .760 .006 . .749 .108

35 35 35 35 35 35 35 35 35 35 35 35 35 35

.816** -.308 -.907** .803** .575** .252 .319 -.077 .027 -.370* .253 .056 1.000 .714**

.000 .071 .000 .000 .000 .144 .062 .661 .878 .029 .143 .749 . .000

35 35 35 35 35 35 35 35 35 35 35 35 35 35

.265 .024 -.528** .255 -.056 .273 .144 -.386* -.175 -.428* .355* -.276 .714** 1.000

.124 .893 .001 .140 .750 .112 .409 .022 .315 .010 .036 .108 .000 .

35 35 35 35 35 35 35 35 35 35 35 35 35 35

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

YEAR

INFLOW

LULC

POPDEN

WTRTBL

RAINFALL

RDAYS

MAXTMP

MINTMP

WINDSPD

RELHUM

SOLAR

NA

ASMO




Table 6.6 Correlation Matrix : Bisalpur dam


166


In section 6.4, trend lines and directions of various parameters which influence to runoff

have been shown. Now it is essential to determine the principal components out of the

various components discussed earlier so that the same may be considered for further

planning and analysis.


Regression lines of various parameters (predictors) with inflow (dependent variable) have

been plotted as shown in Fig. 6.11. Coefficients of determination have also been

mentioned on graphs. By coefficient of correlations and corresponding t-values compared

with critical t-values at 5% and 1% level of significance, we can determine the

significance level of the parameters for inflow to the dam.


(c) Land use (d) GWL below GL

y = 2.8004x - 434.67

R² = 0.1194

0

1000

2000

3000

4000

5000

6000

200 300 400 500 600 700 800 900

Infl

ow

(M

CM

)

Rainfall (mm)

Rainfall-Inflow

y = -13.939x + 1606.3

R² = 0.0203

0

1000

2000

3000

4000

5000

6000

0 20 40 60

Infl

ow

(M

CM

)

Rainy days

Rainy days-Inflow

y = 90.231x - 2265.8

R² = 0.1307

0

1000

2000

3000

4000

5000

6000

25 35 45 55

Infl

ow

(M

CM

)

Land use (Contributing effect)

Land use (Conctributing

effect)-Inflow

y = -180.82x + 3628

R² = 0.1135

0

1000

2000

3000

4000

5000

6000

10 12 14 16 18 20

Infl

ow

(M

CM

)

GWL below GL(m)

GWL below GL-Inflow


167

(e) Mean daily max. Temp. (f)Mean daily min. temp.


(i)Solar radiation (j) Population density

Fig. 6.11 Cross plots: Between inflow and independent variables:Bisalpur

y = -365.4x + 13473 R² = 0.0731

0

1000

2000

3000

4000

5000

6000

31 32 33 34 35 36

Infl

ow

(M

CM

)

Mean daily max temp.

Mean daily max temp-Inflow

y = -640.84x + 13315

R² = 0.0898

0

1000

2000

3000

4000

5000

6000

17 18 19 20 21

Infl

ow

(M

CM

)

Mean daily min. temp.


y = -564.71x + 2808.1

R² = 0.004

0

1000

2000

3000

4000

5000

6000

2 2.5 3 3.5

Infl

ow

(M

CM

)

Wind speed

Wind speed-Inflow

y = 1629.6x + 540.54

R² = 0.0029

0

1000

2000

3000

4000

5000

6000

0.3 0.35 0.4 0.45 0.5

Infl

ow

(M

CM

)

Relative humidity


y = -488.83x + 10587

R² = 0.0372

0

1000

2000

3000

4000

5000

6000

18 19 20

Infl

ow

(M

CM

)

Solar radiation

Solar radiation-Inflow

y = -8.71x + 3872.8

R² = 0.2244

0

1000

2000

3000

4000

5000

6000

100 300 500

Infl

ow

(M

CM

)

Population density (nos.)



168

Regression analysis of inflow with various factors independently, as shown in Fig

6.11 is summarized in Table 6.7. Several cross plots showing the relationship between

independent variables and inflow may be generated to develop empirical relations for the

indirect estimation of the output. These relations are obtained using statistical methods

such as regression analysis. However, although empirical equations are cost- effective

and not too time-consuming, they include uncertainties relating to the limited data

available (Majdi et al. 2010). To determine the parameters affecting inflow more than

others, several cross plots showing the relationship between independent variables and

inflow have been generated from a total of 35 data (Fig. 6.11 and Table 6.7).

Table 6.7 Correlation and regression of inflow with various parameters: Bisalpur

S.N. Factor

Regression

equation

Type of

correlation

Coefficient of

correlation (R)

t-

value Significance

1 Rainfall y = 2.80x -434.6 Positive 0.34 2.11 S

2 Rainy days y = -13.93x+1606 Negative 0.14 0.82 NS

3 Land use (Cont. effect) y = 90.23x -2265 Positive 0.36 2.22 S

4 GWL below GL y = -180.8x + 3628 Negative 0.34 2.05 S

5 Population density y = -8.71x + 3872 Negative 0.47 3.09 HS

6 Mean of daily max. temp.

(0C)

y = -365.4x + 13473 Negative 0.27 1.61 NS

7 Mean of daily min. temp. (0C) y = -640.8x + 13315 Negative 0.30 1.80 NS

8 Mean of daily wind speed

(m/sec) y = -564.7x + 2808 Negative 0.06 0.36 NS

9 Mean of daily RH (fraction) y = 1629x –540.5 Positive 0.04 0.26 NS


(MJ/m2) y = -488.8x + 10587 Negative 0.19 1.13 NS


The critical value of tc for the n=35-2=33 degree of freedom at the 5% level of

significance is 2.034 and 1% level of significance is 2.735. Rainfall, land use

(contributing effect), groundwater level are having a significant correlation with the

inflow, and population density is highly significant while other climatic factors like mean

daily min. temp., wind speed, and solar radiation are having no correlation with inflow.

Population density, land use, rainfall, and GWL below GL are having correlations with

inflow and t-values are higher than the critical tc value. This concludes that population


169

density; land use, rainfall, and GWL below GL are principal components for the inflow to

the Bisalpur dam.



components and their relative influence so that the MLR can be further simplified as per

availability of data. By relative influence value (Rij) computed by CAM (Annexure 8c),

the relative importance of the four factors is mentioned as below (Fig. 6.12):

(1) LULC (Contributing area %)= 0.75

(2) Rainfall= 0.74

(3) GWL= 0.47

(4) Population density= 0.43

Fig. 6.12 Relative influences of the factors responsible for inflow: CAM for Bisalpur

From the two analyses, it is found that four components viz. LULC, rainfall, GWL

below GL, and population density are the principal components for inflow to the Bisalpur

dam. Out of these four principal components, LULC and rainfall are most influencing

components to the inflow to Bisalpur dam. Sensitivity analysis shows less water inflow to

the Bisalpur dam due to the LULC is the main factor having 32% of the effect; rainfall is

responsible for 31% of the effect, GWL below GL is having 19% of the effect and

population density has 18% effect on the inflow.

0.43

0.75 0.74

0.47

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Population density LULC (Contributing

area %)

Rainfall GWL

Rela

tive in

flu

en

ce

Factor

Relative influence (CAM): Bisalpur


170


The inflow equation for Bisalpur dam may be formed with the help of Multiple Linear

Regression (MLR) considering the six principal components as derived in Section 6.5.

Table 6.8 MLR summary output: Inflow to the Bisalpur dam

Regression Statistics Multiple R 0.67 R Square 0.44 Adjusted R Square 0.37 Standard Error 896.65 Observations 35.00

ANOVA

df SS MS F Significance

F Regression 4 19312407 4828102 6.01 0.001 Residual 30 24119425 803980.8

Total 34 43431832

Coefficients Standard

Error t Stat. P-

value Lower 95% Upper 95%

Intercept 9280.76 6829.89 1.36 0.18 -4667.74 23229.26 LULC (Contributing effect) -123.43 115.30 -1.07 0.29 -358.90 112.03

Rainfall 3.17 1.21 2.63 0.01 0.71 5.64

Water table 108.26 137.80 0.79 0.44 -173.16 389.69

Population density -21.59 10.39 -2.08 0.05 -42.80 -0.38

Using the computed coefficients, inflow equation is developed as shown below:

443322110 **** xxxxInflow (6.1)

Where values of the coefficients are 9820.76, -123.43, 3.17, 108.26, -21.59 and

variables are LULC (Contributing effect), rainfall, GWL below GL and population

density respectively, and a minimum value of computed inflow is set to zero. The

correlation coefficient (multiple R) for the above relationship is 0.67.

From the developed equation, the inflow has been computed, and a graph is

plotted between observed inflow and computed inflow along with the goodness of fit as

shown in Fig.6.13. Between the two values, the coefficient of correlation (R) is 0.67, and

the corresponding t-value is 5.16, which is more than the critical value at 1% level of

significance (2.735), which infers that the inflow values computed from the developed

equation are highly significant.


171

Fig. 6.13 Goodness of fit for the developed equation (MLR)

6.7 Regression Analysis and Determination of Factors Responsible for Principal

Components

As far as changes in land use are concerned, agricultural area (Total crop area) is

increasing with time and population. It has increased from 7664 sqkm to 10437 sqkm

from 1978 to 2012. The area sown more than once is also increasing; it has increased

from around 1973 sqkm to 3520 sqkm. Agricultural activities, land area sown, forest area,

land area put to non-agricultural uses like habitation, industries, mining, roads,

infrastructures, etc. are increasing at a very fast pace. Fig. 6.7 and 6.8 show that land

utilizations which are having a reducing effect on the surface runoff are increasing and

the land utilizations which are having a contributing effect on the surface runoff viz.

barren land, culturable waste, and fallow land are decreasing with the passage of time.

Due to continuous partitions of the fields, the average size of the field is decreasing, and

the number of total fields is increasing with time. The average size of the field has

decreased from 2.45 ha to 2.07 ha from 1980 to 2000. Population density has highly

significant correlations with LULC, crop area, GWL below GL, and average field size. T-

values for these correlations are 15.48, 7.73, 7.74, and 39.22 which is more than the

critical value of tc=2.735 at df=35-2=33 and 1% level of significance. T-value for

correlation of average field size with LULC is 11.67 which is highly significant. In these

analyses, it is inferred that inflow to the dam is influenced by the land use (contributing

y = 1.0417x - 69.083

R² = 0.4477

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

0 500 1000 1500 2000 2500 3000

Goodness of fit for developed equation (MLR): Bisalpur


172

effect) in addition to rainfall, which has a highly significant correlation with population

density and average field size.

Fig. 6.14 Correlation between factors responsible and principal components

y = -0.0691x + 59.524

R² = 0.8799

0

10

20

30

40

50

100 200 300 400 500

Lan

d u

se

(%)

Population density

Population density-LULC:

Bisalpur

y = 0.039x + 23.87

R² = 0.6449

0

10

20

30

40

50

100 200 300 400 500

Crop

area (

%)

Population density

Population density-Crop area:

Bisalpur

y = 0.0275x + 5.0517

R² = 0.6457

0.00

5.00

10.00

15.00

20.00

100 200 300 400 500

GW

L (

m)

Population density

Population density-GWL BGL:

Bisalpur

y = -0.0032x + 3.1469

R² = 0.9799

2.00

2.10

2.20

2.30

2.40

2.50

200 250 300 350

Avera

ge f

ield

siz

e (

ha

)

Population density

Population density-Avg. field

size: Bisalpur

2.00

2.10

2.20

2.30

2.40

2.50

1980 1985 1990 1995 2000

Average f

ield

siz

e (

ha

)

Average field size:

Bisalpur

y = 21.305x - 7.5238

R² = 0.8059

30

35

40

45

50

2.00 2.20 2.40 2.60

LU

LC

(%

)

Average fied size (m)

Average field size-LULC (%)


173

7 CONCLUSIONS AND RECOMMENDATIONS

7.1 Conclusions

7.2 Recommendations

7.3 Major Contributions of Present Study

7.4 Limitations of Findings

7.5 Scope of Further Research


174

CHAPTER-7

CONCLUSIONS AND RECOMMENDATIONS

7.1 The following conclusions are drawn based on the studies

I. The temporal variation of water inflow to the dams of Rajasthan State is declining.

It has slightly improved since the last couple of years (2010-2013). The inflow

trend has diminished in the Ramgarh dam while it is showing a declining trend in

the Bisalpur dam.

II. Performance dependability of the state surface water resources has reduced to

33.36% and 24.02% for 20 years data series and 10 year data series against the

50% expected dependability. This shows a sign of temporal variation of inflow to

the dams of Rajasthan State. Performance dependability of Ramgarh dam is zero.

Performance dependability of Bisalpur dam for 35 yrs, 18 yrs, and 13 yrs data

series is 36.45%, 16.23%, and 14.88% respectively. Therefore, the reliability of

the state surface water resources is very less and showing the declining trend.

Sustainability of the dams of Rajasthan state is 38.7%, and vulnerability is

15.52%. The two parameters for Ramgarh dam are 0% and 83.89%. Sustainability

of Bisalpur dam is 42.34%, 21.40% and 23.17% for 35 yrs, 18 yrs, and 13 yrs data

series respectively. The vulnerability of Bisalpur dam is 35.78%, 55.90%, and

55.52% for 35 yrs, 18 yrs, and 13 yrs data series respectively.

III. The year 2002 was the year of maximum deficit. In this year, dams of Rajasthan

State received only 30.9% water while Bisalpur dam received only 17 MCM of

water, which were the lowest inflow to the dams.

IV. Various performance statistical parameters reveal that method of computation of

theoretical inflow “Strange‟s Table Method” proved to be giving the less precise/

erroneous results.

V. Year 1994 is identified as critical year, which divides two consecutive non-

overlapping epochs- pre-disturbance and post-disturbance, for the dams of

Rajasthan State. Critical years for Ramgarh and Bisalpur dams are 1998 and 1996

respectively.


175

VI. The results of the ANOVA test reveal that there exists a temporal and spatial

distribution of rainfall over the state, which is one of the reasons for temporal

variability of inflow to the dams of the state.

VII. Trend analysis reveals that rainfall is showing an increasing trend, in spite of that

inflow trend is declining. The numbers of rainy days in monsoon period are

increasing. Climate parameters are showing a slight variation. LULC is the

principal component responsible for inflow to the Ramgarh and Bisalpur dams

while rainfall is the principal component for inflow to the dams of the state as a

whole. Decadal population growth rate of the state is more than the country‟s

growth rate.

VIII. Inflow to the dams have negative correlations with GWL below GL, mean of daily

maximum temperature, the daily minimum temperature, daily wind speed, daily

solar radiation and population density while positive correlations with rainfall,

LULC (contributing effect), and mean of daily relative humidity.

IX. Cosine Amplitude Method (CAM) of sensitivity analysis shows that rainfall and

other climatic parameters have high relative influence value (Rij) for inflow to the

dams of Rajasthan State while land use, GWL below GL, and population density

are not principal components. Specifically, inflows to the Ramgarh and Bisalpur

dams are highly influenced by LULC, rainfall, GWL and population density.

X. Considering the principal components as a predictor, inflow (dependent variable)

equations have been derived using Multiple Linear Regression (MLR) technique.

The generated equations give highly significant results of inflows, therefore, could

be used for an assessment of inflow patterns to the dams of Rajasthan State and

specifically in Ramgarh and Bisalpur dams.

XI. Regression analysis and t-tests infer that besides rainfall, inflows to the dams are

also influenced by the land use (contributing effect) and GWL, which have highly

significant correlations with population density and average field size. The

average size of the field is regularly decreasing with time and increasing

population.


176

7.2 The following recommendations are made based on the studies

Increase in population density is putting stress on quality and quantity (Q & Q) of water

and on the other hand it is affecting the land use (contributing effect) pattern and also

lowering the GWL below GL of the catchment area of the dams. This situation is leading

to the declining trend of inflow to the dams of Rajasthan State.

I. More scientific methods should be used to compute theoretical inflow to the dams

instead of using „Strange‟s table‟, which gives less precise/ erroneous results.

II. 1994-1998 are proved to be critical years, which divide the two consecutive non-

overlapping epochs- pre-disturbance and post-disturbance. This may be used to

analyze the changes occurred leading to declining trend of inflow to the dams. It is

recommended to review the policies adopted, LULC; agricultural practices etc.

after the respective critical years.

III. Population density is found to be one of the principal component responsible for

less water inflow to the Ramgarh and Bisalpur dams. It is recommended to adhere

with most efficient use of available water to cater the increasing population and

also to plan to stabilize/reduce the decadal growth rate of population. This will

lead to stabilization of the average size of the field; increase in LULC

(contributing effect), and improvement of GWL below GL. As concluded earlier,

these improvements will lead to augmentation of inflow pattern to the dams.

IV. IWRM, INRM, RWH, recycle and reuse of water, micro watershed planning,

benchmarking and water auditing, water pricing, water trading, water

privatization, interlinking of rivers/ transboundary water sharing,

infiltration/ground water recharge, conjunctive use of surface and subsurface

water, more crop per drop by adopting sprinkler/drip/shednet irrigation techniques

and other suitable water management techniques should be adhered to ensure

equitable and sustainable Q & Q of water to all stakeholders including

environment and river itself. Policies need to fulfill the slogan ‟some, for all,

forever. Dedicated pressure irrigation techniques like sprinkler/drip were

implemented in Narmada Canal Project (Jalore district) and selected canals of

Indira Gandhi Nahar Project (IGNP). This has resulted in 30% saving of water

along with around 20% increase in per acre production. Similarly, RWH has been


177

made compulsory for residential houses of the area more than 300 sqm in Jaipur

city. These RWH (Tankas) and reuse techniques were being used in western

Rajasthan since long back for self-reliance for water. Recycle (Sewerage

Treatment Plant-STP) is being made compulsory for new housing and industrial

project. In housing effluents, STP recycles around 75-80% water. District wise

and village wise IWRM plans have been prepared, and micro-watershed planning

is being done in the state. This type of planning is enabling the water

administrators to supervise, monitor and plan the available water and demand at

micro-watershed level. The project of ATW (Any Time Water) and water cans are

ensuring availability of drinking water 24x7 at a very nominal price. These types

of case studies are making successful stories of proper allocation and planning of

available water and recommended to be encouraged.

V. 5-R principal (refuse, reduce, reuse, recycle, rationing) can help in facing the

consequences arising out of water scarcity situations. Resource saving is resource

generation. Iglesias et al. (2007) and Liu et al. (2005) emphasized that water

saving is the key strategy for overall reduction of societal vulnerability to

drought/water scarcity.

7.3 Major Contribution of Present Study

I. The present study attempts to address the existing gap in available scientific

literature to describe the temporal variation of inflow to the dams of Rajasthan

State.

II. This study is an attempt to draw conclusions regarding the direction and

variability of inflow to the dams of Rajasthan State with special reference to

Ramgarh and Bisalpur dams.

III. This study attempts to draw a conclusion regarding the correctness of inflow

assessment by „Strange‟s table‟ being used as per prevailing practice in the WRD.

It is concluded in the study that this method gives less precise/ erroneous results.

IV. Critical years have been determined in this study, which may help in the

determination of factors responsible for disturbances, which lead to the declining

trend of inflow to the dams.


178

V. Direction and variability assessment of various parameters viz. Climatic, land use,

groundwater and population parameters which influence the inflow to the dams

have been made in this study.

VI. Correlation and relative influence of various parameters have been ascertained to

find out the principal components responsible for inflow to the dams so that it may

help in an efficient planning and management of surface water availability in the

state.

VII. Equations for inflow assessment of dams in the state, specifically for Ramgarh

and Bisalpur dams have been derived in this study, which may help for reliable

assessment of inflows to these dams.

VIII. Ramgarh dam was the source of drinking water supply of Jaipur city and Bisalpur

dam is a present source. Inflow to the Ramgarh dam has diminished to zero level,

and Bisalpur dam is showing a declining trend even after more than normal

rainfall. It is now being planned by the Government of Rajasthan to augment the

inflow to Bisalpur dam by the interlinking of rivers Banas (catchment area of

Bisalpur dam) and Chambal. Future of drinking water supply in Jaipur „the capital

city of the state‟ is dependent on surplus water availability in the Chambal River,

which is also uncertain looking to the inflow trend in the state. Therefore, this

study may help in an optimized and efficient planning and management of

storages, distribution and uses of available water and the various factors

influencing these aspects.

IX. In the future, the study can also be used by managers as a decision support system

(DSS) to manage the inflow to the dams.

7.4 Limitations of Findings

I. Theoretical inflow has been computed using Strange‟s table due to absence of

gauge-discharge data. However it is proved that this method gives less precise/

erroneous results. More scientific method is desired to be used for assessment of

theoretical inflow, specifically in Ramgarh dam.


179

II. LULC is determined as principal component responsible for temporal variation of

inflow to the dams. LULC data has been collected from statistical department

instead of GIS.

III. Hydrological processes like water balance equations etc. have not been covered in

the thesis.

IV. There is little physics or science like soil moisture, evapotranspiration etc.

7.5 Scope of Further Research

I. Further research may be carried out to simulate the actual inflow to the Ramgarh

dam as there is further need for the development of water balance equation.

II. A similar study can be undertaken for other major/medium dams so that inflow

pattern, critical year, and principal components responsible for less water inflow

can be ascertained. This will help in reviewing the impact of policy formed earlier

and will also help in policy formation in future.

III. More scientific investigations of the impacts of anthropogenic changes and

climate change on inflow to the dams based on focused field study, systematic

experimental design, and long term programmes can be undertaken.

IV. Effect of soil moisture and GWL below GL on inflow to the dams can be

investigated further.

180

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193

APPENDICES

Appendix-1

Detail of 115 major and medium dams of Rajasthan State

S.N. Name of the

Dam Name of

Basin Name of District

Type of Dam

Capacity (MCM)

Actual Dn based on

Inflow

(20 years)

Actual Dn based on

Inflow

(10 years)

IF of the

district

(20 years)

IF of the

district

(10 years)

1 Udaisagar Banas Udaipur Medium 31.15 15.0% 9.1% 165.0 100.1

2 Vallabhnagar Banas Udaipur Medium 30.44 9.5% 9.1% 102.1 97.8

3 Bhagolia Banas Udaipur Medium 18.55 4.8% 9.1% 31.2 59.6

4 Badgaon Banas Udaipur Medium 31.49 33.0% 45.0% 367.0 500.4

5 Jaisamand Mahi Udaipur Major 414.90 19.1% 9.1% 2790.8 1333.2

6 Jakham Mahi Udaipur Major 142.03 38.1% 27.3% 1910.7 1367.6

7 Som kagdar Banas Udaipur Medium 36.19 81.0% 72.7% 1035.2 929.4

Udaipur District(Total)=

704.76

6402.0 4388.0

Wtd. Dn (%)= 25.7 17.6

8 Rajasmand Banas Rajasmand Major 98.73 0.0% 0.0% 0.0 0.0

9 Nandsamand Banas Rajasmand Medium 21.24 66.7% 45.5% 500.0 340.9

10 Mataji ka

khera Banas Rajasmand Medium 11.92 0.0% 0.0% 0.0 0.0

Rajasmand District(Total)=

131.89

500.0 340.9

Wtd. Dn (%)= 10.7 7.3

11 Bisalpur Banas Tonk Major 938.83 33.3% 27.3% 11048.

9 9040.0

12 Galwa Banas Tonk Major 48.74 28.6% 0.0% 491.7 0.0

13 Tordi sagar Banas Tonk Major 47.15 19.1% 0.0% 317.2 0.0

14 Mashi Banas Tonk Medium 48.15 33.3% 0.0% 566.6 0.0

15 Chandsen Banas Tonk Medium 14.70 14.3% 0.0% 74.2 0.0

16 Galwania Chambal Tonk Medium 12.55 38.1% 27.3% 168.8 120.8

17 Motisagar Banas Tonk Medium 12.89 38.1% 9.1% 173.4 41.4

Tonk District(Total)=

1123.00

12840.

7 9202.2

Wtd. Dn (%)= 32.4 23.2

18 Meja Banas Bhilwara Major 84.03 9.5% 9.1% 281.9 270.0

19 Matrakundia Banas Bhilwara Medium 50.67 0.0% 0.0% 0.0 0.0

20 Sareri Banas Bhilwara Medium 55.11 4.8% 0.0% 92.6 0.0

21 Arwar Banas Bhilwara Medium 47.81 4.8% 0.0% 80.3 0.0

22 Khari Dam Banas Bhilwara Medium 33.31 14.3% 0.0% 168.1 0.0

23 Naharsagar Banas Bhilwara Medium 24.53 23.8% 9.1% 206.2 78.8

24 Kothari Banas Bhilwara Medium 21.52 28.6% 18.2% 217.1 138.2

25 Jetpura Banas Bhilwara Medium 16.40 57.1% 36.4% 330.8 210.5

26 Umedsagar Banas Bhilwara Medium 17.79 14.3% 0.0% 89.7 0.0

27 Mandal Banas Bhilwara Medium 11.89 14.3% 18.2% 60.0 76.4

28 Chandrabhag

a Banas Bhilwara Medium 9.01 0.0% 0.0% 0.0 0.0

29 Jhadol Banas Bhilwara Medium 9.29 19.1% 18.2% 62.5 59.6

Bhilwara District(Total)=

381.34

1589.3 833.5

Wtd. Dn (%)= 11.8 6.2

194

30 Gosunda Banas Chittorgarh Major 0.00 D.N.A.

31 Bassi Banas Chittorgarh Medium 20.25 57.1% 45.5% 408.6 325.0

32 Bhopal Sagar Banas Chittorgarh Medium 18.55 9.5% 9.1% 62.4 59.6

33 Dhindoli Banas Chittorgarh Medium 7.53 5.9% 0.0% 15.7 0.0

34 Gambhiri Banas Chittorgarh Medium 55.03 33.3% 18.2% 647.6 353.2

35 Murlia Banas Chittorgarh Medium 9.63 14.3% 9.1% 48.6 30.9

36 Orai Banas Chittorgarh Medium 35.29 61.9% 27.3% 771.3 339.8

37 Rana Pratap

Sagar Chambal Chittorgarh Major 2899.07 28.6% 27.3% 29246.

0

27915.

2 38 Wagon Banas Chittorgarh Medium 37.58 28.6% 9.1% 379.1 120.8

Chhitorgarh District(Total)=

3082.92

31579.2

29144.5

Wtd. Dn (%)= 29.0 26.8

39 Buchara Sabi Jaipur Medium 16.65 16.7% 0.0% 98.0 0.0

40 Chhittoli Sabi Jaipur Medium 24.38 0.0% 0.0% 0.0 0.0

41 Ramgarh Banganga Jaipur Major 75.05 0.0% 0.0% 0.0 0.0

42 Chhaparwara Banas Jaipur Medium 35.00 4.8% 0.0% 58.8 0.0

43 Kalakh Sagar Banas Jaipur Medium 16.45 0.0% 0.0% 0.0 0.0

44 Kanota Banas Jaipur Medium 14.13 0.0% 0.0% 0.0 0.0

45 Hingoniya Banas Jaipur Medium 7.50 0.0% 0.0% 0.0 0.0

Jaipur District(Total)=

189.18

156.8 0.0

Wtd. Dn (%)= 2.3 0.0

46 Narayan

Sagar Banas Ajmer Medium 19.97 0.0% 0.0% 0.0 0.0

47 Phool Sagar Luni Ajmer Medium 0.00 D.N.A.

0.0 0.0

48 Lassariya Banas Ajmer Medium 11.50 38.1% 18.2% 154.7 73.8

Ajmer District(Total)=

31.46

154.7 73.8

Wtd. Dn (%)= 13.9 6.6

49 Mora Sagar Banas Sawai

Madhopur Medium

13.74 9.5% 9.1% 46.2 44.1

50 Dheel Banas Sawai

Madhopur Medium

27.75 42.9% 9.1% 420.0 89.2

51 Kalisil Banas Sawai

Madhopur Medium

41.77 42.9% 27.3% 632.2 402.2

52 Mansarovar Chambal Sawai

Madhopur Medium

15.32 33.3% 18.2% 180.3 98.4

53 Surwal Banas Sawai

Madhopur Medium

22.91 14.3% 0.0% 115.6 0.0

Sawai Madhopur District(Total)=

121.50

1394.3 633.9

Wtd. Dn (%)= 32.5 14.8

54 Raipur Patan Shekhawati Sikar Medium 9.18 4.8% 0.0% 4.8 0.0

Wtd. Dn (%)=

55 Harsora Sabi Alwar Medium 8.81 21.1% 9.1% 65.5 28.3

56 Jaisamand Ruparail Alwar Medium 26.96 4.8% 0.0% 45.3 0.0

57 Tasai Banganga Alwar Medium 0.00 D.N.A.

0.0 0.0

Alwar District(Total)=

35.77

110.8 28.3

Wtd. Dn (%)= 8.8 2.2

58 Bharetha gambhir Bharatpur Medium 52.68 19.1% 9.0% 354.3 167.6

59 Sikari Bund Ruparail Bharatpur Major 42.48 D.N.A.

0.0 0.0

60 Ajan Lower Banganga Bharatpur Medium 17.98 19.0% 0.0% 120.9 0.0

61 Bhatawali Banganga Bharatpur Medium 0.00 D.N.A.

0.0 0.0

195

62 Sarsera Banganga Bharatpur Medium 0.00 D.N.A.

0.0 0.0

63 Baretha Banganga Bharatpur Medium 0.00 D.N.A.

0.0 0.0

Bharatpur District(Total)=

113.14

475.2 167.6

Wtd. Dn (%)= 11.9 4.2

64 Morel Banas Dausa Major 76.66 19.1% 0.0% 515.7 0.0

65 Chandrana Banganga Dausa Medium 4.93 14.3% 0.0% 24.9 0.0

66 Kala Kho Banganga Dausa Medium 13.28 33.3% 0.0% 156.3 0.0

67 Rondh Banganga Dausa Medium 0.00 D.N.A.

0.0 0.0

68 Moroli Banganga Dausa Medium 0.00 D.N.A.

0.0 0.0

69 Sainthal Sagar

Banganga Dausa Medium 13.71 19.1% 0.0% 92.2 0.0

70 Madho Sagar Banganga Dausa Medium 22.60 0.0% 0.0% 0.0 0.0

Dausa District(Total)=

131.18

789.1 0.0

Wtd. Dn (%)= 17.0 0.0

71 Jugger gambhir Karauli Medium 35.00 24.5% 0.0% 302.8 0.0

72 Panchana gambhir Karauli Medium 59.47 0.0% 0.0% 0.0 0.0

Karauli District(Total)=

94.48

302.8 0.0

Wtd. Dn (%)= 9.1 0.0

73 Parbati Parbati Dholpur Major 113.85 22.2% 18.2% 893.2 730.8

74 Urmila Sagar Parbati Dholpur Medium 15.75 9.5% 0.0% 52.9 0.0

Dholpur District(Total)=

129.60

946.2 730.8

Wtd. Dn (%)= 20.7 16.0

75 Sawan Bhado Chambal Kota Medium 30.02 9.5% 18.2% 100.7 192.7

76 Alnia Chambal Kota Medium 43.73 23.8% 9.1% 367.5 140.5

77 Kota Barrage Chambal Kota Major 0.00 D.N.A.

78 Takli Chambal Kota Medium 0.00 D.N.A. Ongoing

Dholpur District(Total)=

73.75

468.2 333.2

Wtd. Dn (%)= 18.0 12.8

79 Jawahar Sagar

Chambal Bundi Major 0.00 D.N.A.

80 Burdha Chambal Bundi Medium 29.00 61.9% 36.4% 633.9 372.3

81 Bhimlet Chambal Bundi Medium 11.70 38.1% 45.5% 157.4 187.7

82 Bundi Ka Gothra

Chambal Bundi Medium 18.97 33.3% 18.2% 223.3 121.8

83 Dugari Chambal Bundi Medium 18.10 5.3% 0.0% 33.6 0.0

84 Gudha Chambal Bundi Major 95.58 61.9% 45.5% 2089.1 1533.9

85 Gararda Chambal Bundi Medium 0.00 D.N.A. Ongoing

Bundi District(Total)=

173.35

3137.3 2215.8

Wtd. Dn (%)= 51.3 36.2

86 Parwan Pickup

Chambal Baran Medium 0.00 D.N.A. Pickup/lift

87 Parwati

Pickup Chambal Baran Medium 0.00 D.N.A. Pickup/lif

t

88 Parwan Lift Chambal Baran Medium 0.00 D.N.A. Pickup/lif

t

89 Gopalpura Chambal Baran Medium 32.65 71.4% 36.4% 823.6 419.2

90 Bilas Chambal Baran Medium 29.17 66.7% 54.5% 686.6 561.8

91 Benthali Chambal Baran Medium 31.58 _ 22.2% 0.0 247.8

92 Umedsagar Chambal Baran Medium 18.61 66.7% 36.4% 438.0 238.9

93 Lhasi Chambal Baran Medium 0.00 D.N.A. Ongoing

Baran District(Total)=

112.01

1948.2 1467.6

196

Wtd. Dn (%)= 68.6 37.1

94 Chhapi Chambal Jhalawar Medium 82.64 0.0% 0.0% 0.0 0.0

95 Chauli Chambal Jhalawar Medium 53.44 _ 14.28%(6

yr) 0.0 269.5

96 Bhimsagar Chambal Jhalawar Medium 76.55 38.1% 18.2% 1029.8 491.4

97 Harishchand

Sagar Chambal Jhalawar Medium 0.00 D.N.A.

98 Piplad Chambal Jhalawar Medium 0.00 D.N.A. Ongoing

99 Gagrin Chambal Jhalawar Medium 0.00 D.N.A. Ongoing

Jhalawar District(Total)=

212.63

1029.8 760.9

Wtd. Dn (%)= 18.3 10.1

100 Mahi Mahi Basnwara Major 2061.48 57.1% 36.4% 0.6 0.4

101 Som Kamla

Amba Mahi Dungarpur Major 172.81 25.0% 27.3% 0.3 0.3

102 Sardarsamand Luni Pali Major 88.22 4.8% 0.0% 148.3 0.0

103 Jawai Dam Luni Pali Major 207.45 28.6% 18.2% 2092.8 1331.7

104 Hemawas Luni Pali Medium 62.53 33.3% 9.1% 735.9 200.9

105 Kharda Luni Pali Medium 18.80 28.6% 18.2% 189.7 120.7

106 Giroliya Luni Pali Medium 0.00 D.N.A.

0.0 0.0

107 Raipur Luni Luni Pali Medium 12.55 52.4% 27.3% 232.0 120.8

108 Rajsagar

Chopra Luni Pali Medium 7.59 23.5% 0.0% 63.1 0.0

Pali District(Total)=

397.14

3461.8 1774.1

Wtd. Dn (%)= 24.7 12.7

109 Jaswant Sagar Luni Jodhpur Medium 52.82 4.8% 9.1%

Jodhpur District(Total)=

Dn (%)= 4.8 9.1

110 Bankli Luni Jalore Medium 34.55 33.3% 9.1%

111 Bandi

Sandhara Luni Jalore Medium 0.00 D.N.A. Ongoing

112 Chittalwara Luni Jalore Medium 12.23 0.0% 0.0% 0.0 0.0

Jalore District(Total)=

46.79 Wtd.

24.6 6.7

113 Angore Luni Sirohi Medium 14.02 38.1% 36.4% 188.6 180.0

114 Ora Luni Sirohi Medium 22.66 9.5% 0.0% 76.2 0.0

115 West Banas West Banas Sirohi Medium 39.08 33.3% 18.2% 460.0 250.9

Sirohi District(Total)=

75.76

724.7 430.9

Wtd. Dn (%)= 18.3 16.1

197

Appendix-2

113-yea rrainfall data for ANOVA test (A3 Sheet attached)

198

Appendix-3 Weather Data of Ramgarh Dam Catchment Year Annual

mean Max

temp.

Annual

mean min.

temp.

Annual

Precipitation

Annual

mean Rainy

days<5.0mm

Annual

mean wind

speed

Annual

mean

relative

humidity

Annual

mean

solar

radiation

0C

0C mm days m/s fraction MJ/sqm

1979 32.99 17.85 363.58 20.00 2.70 0.39 18.92

1980 33.95 16.93 525.32 34.50 2.68 0.38 19.13

1981 32.80 17.72 697.74 37.50 2.66 0.42 18.43

1982 32.21 17.88 380.75 21.50 2.54 0.42 17.79

1983 31.98 16.93 654.00 39.50 2.44 0.44 18.55

1984 33.21 16.80 413.00 22.00 2.84 0.37 18.39

1985 33.45 17.22 566.00 26.50 2.80 0.40 19.02

1986 33.48 16.56 305.00 20.00 2.66 0.39 19.05

1987 34.83 17.38 210.00 10.50 2.58 0.34 18.26

1988 33.69 17.97 547.00 55.00 2.71 0.41 18.40

1989 33.30 16.51 454.00 23.00 2.69 0.37 18.89

1990 32.87 17.92 451.00 45.00 2.75 0.43 18.36

1991 33.52 17.35 302.00 20.50 2.56 0.39 18.19

1992 32.65 17.08 577.00 35.50 2.57 0.42 18.22

1993 33.51 18.16 606.00 34.50 2.69 0.39 18.31

1994 32.84 17.15 502.00 43.00 2.56 0.43 18.54

1995 32.83 17.57 856.00 44.00 2.55 0.42 18.39

1996 32.79 17.37 862.00 61.50 2.60 0.44 18.15

1997 31.39 16.91 541.00 37.50 2.58 0.46 17.32

1998 33.17 18.12 558.00 37.50 2.58 0.43 18.12

1999 35.77 18.75 446.00 19.00 2.75 0.34 19.14

2000 34.83 16.51 402.00 26.00 2.76 0.35 19.25

2001 34.49 16.89 364.00 24.50 2.53 0.36 19.10

2002 35.82 16.88 223.00 12.00 2.68 0.34 19.02

2003 33.22 17.18 922.00 58.00 2.46 0.42 19.03

2004 34.41 18.09 547.00 15.00 2.60 0.37 19.10

2005 33.60 16.69 856.00 18.50 2.55 0.38 18.51

2006 34.54 17.25 432.00 8.50 2.57 0.38 18.20

2007 34.09 17.84 412.00 22.50 2.60 0.39 17.74

2008 33.35 17.82 847.00 49.50 2.50 0.41 17.90

2009 34.46 18.48 361.00 26.50 2.68 0.38 18.59

2010 33.66 18.87 868.00 62.00 2.44 0.44 18.86

Averag

e 33.55 17.46 532.86 31.59 2.62 0.40 18.53

Min. 31.39 16.51 210.00 8.50 2.44 0.34 17.32

Max. 35.82 18.87 922.00 62.00 2.84 0.46 19.25

199

Appendix-4 Weather Data of Bisalpur Dam Catchment

Year Annual

mean Max

temp.

Annual

mean min.

temp.

Annual

Precipitation

Annual mean

Rainy

days<5.0mm

Annual

mean

wind

speed

Annual

mean

relative

humidity

Annual

mean

solar

radiation

0C

0C mm days m/s fraction MJ/sqm

1979 33.47 19.14 736.98 23 2.95 0.39 19.20

1980 34.07 19.19 360.22 22 2.95 0.36 19.50

1981 33.03 19.04 407.80 32 2.95 0.42 18.96

1982 32.87 19.05 585.20 24 2.87 0.42 18.63

1983 32.87 18.24 744.20 34 2.74 0.40 19.22

1984 33.27 18.12 497.50 18 3.03 0.36 19.55

1985 34.02 18.70 383.10 20 2.93 0.36 19.68

1986 33.55 18.45 516.20 21 3.01 0.37 19.68

1987 34.84 19.09 361.50 11 2.89 0.32 19.10

1988 33.82 19.35 564.10 52 2.92 0.40 18.89

1989 33.31 18.42 627.20 29 2.94 0.36 19.22

1990 33.02 19.17 740.50 46 2.99 0.42 18.47

1991 33.85 19.06 555.00 24 2.90 0.37 19.31

1992 33.15 18.67 675.30 33 2.80 0.40 19.02

1993 33.80 19.48 424.50 28 2.99 0.38 18.87

1994 32.66 18.88 805.80 44 2.78 0.43 18.41

1995 33.38 19.22 534.40 31 2.87 0.39 19.00

1996 33.04 18.70 803.40 46 2.90 0.41 19.02

1997 32.12 18.61 596.40 43 3.06 0.46 18.28

1998 33.98 19.41 416.60 30 2.87 0.40 18.98

1999 35.54 20.14 513.90 18 3.09 0.34 19.73

2000 34.87 18.74 394.10 25 3.08 0.32 19.73

2001 34.29 18.61 618.20 35 2.86 0.35 19.61

2002 35.45 19.56 335.60 20 3.19 0.31 20.00

2003 34.01 19.00 554.80 46 3.01 0.38 19.42

2004 34.63 19.22 696.00 19 2.88 0.35 19.50

2005 33.80 18.33 614.20 22 2.78 0.36 19.64

2006 34.10 19.26 786.30 23 2.74 0.39 18.90

2007 33.87 19.40 507.70 30 2.84 0.39 19.06

2008 33.69 18.94 566.80 40 2.89 0.38 18.90

2009 34.75 19.86 402.50 22 3.01 0.37 19.70

2010 33.89 19.86 614.60 48 2.84 0.43 19.80

2011 32.42 18.37 689.40 57 2.66 0.46 19.68

2012 32.73 17.84 684.60 34 2.76 0.41 19.95

2013 32.29 18.17 738.00 46 2.58 0.45 19.63

2014 34.40 19.33

3.08 0.36

Average 33.69 18.96 572.93 31 2.91 0.39 19.26

Min. 32.12 17.84 335.60 11 2.58 0.31 18.28

Max. 35.54 20.14 805.80 57 3.19 0.46 20.00

200

OUTPUT INPUT

S.

N. Year

Actual

Inflow

LULC

(Contributing

effect)

Population

density

Water

Table

Rain-

fall

Rainy

days >

5.0mm

Mean

of daily

max.

temp.

Mean

of

daily

min.

temp.

Wind

speed

Relative

Humidity

Solar

radiation

Net

area

sown

Area

sown

more

than

once

Total

Crop

area

MCM %

m mm days 0C 0C m/sec fraction MJ/m^2 % % %

1 1990 71.01 37.58 126 21.6 731 46 33.02 19.10 2.98 0.42 18.47 45.57 6.71 52.27

2 1991 78.53 35.32 129 20.3 476 23 33.83 18.97 2.88 0.37 19.25 47.81 8.76 56.58

3 1992 66.16 37.81 133 20.9 657 33 33.13 18.59 2.79 0.40 18.98 45.22 7.60 52.82

4 1993 62.60 33.52 136 19.9 539 28 33.79 19.41 2.97 0.38 18.84 49.46 9.43 58.88

5 1994 85.76 35.47 140 20.8 706 44 32.67 18.79 2.77 0.43 18.42 47.41 8.83 56.23

6 1995 77.60 33.11 143 19.9 648 31 33.35 19.13 2.85 0.40 18.97 49.71 9.81 59.52

7 1996 76.70 34.37 147 19.6 769 47 33.03 18.64 2.89 0.41 18.98 48.41 9.05 57.46

8 1997 71.09 33.70 151 18.8 713 43 32.09 18.53 3.04 0.46 18.23 49.04 11.40 60.44

9 1998 57.78 32.76 154 18.6 597 30 33.94 19.35 2.86 0.40 18.94 49.84 15.32 65.16

10 1999 43.58 35.60 158 19.7 508 18 35.55 20.07 3.08 0.34 19.70 46.91 15.55 62.46

11 2000 42.03 37.12 161 20.0 395 25 34.87 18.63 3.06 0.32 19.71 45.27 11.03 56.30

12 2001 58.40 35.99 165 21.3 561 34 34.30 18.53 2.84 0.35 19.58 46.30 9.82 56.13

13 2002 30.87 33.24 169 21.2 276 19 35.47 19.43 3.16 0.31 19.95 48.93 11.77 60.70

14 2003 58.08 50.57 173 23.5 607 47 33.97 18.91 2.98 0.39 19.40 31.54 7.03 38.57

15 2004 71.01 31.31 177 22.5 537 19 34.62 19.17 2.87 0.35 19.48 50.77 12.46 63.23

16 2005 71.00 33.73 181 22.1 515 22 33.79 18.25 2.77 0.36 19.58 48.30 13.17 61.47

17 2006 83.58 32.69 186 22.9 711 22 34.12 19.16 2.73 0.38 18.86 49.13 14.19 63.33

18 2007 69.72 32.81 190 21.9 582 29 33.88 19.32 2.82 0.39 19.00 48.92 13.92 62.85

19 2008 56.20 31.44 194 22.9 603 41 33.67 18.89 2.87 0.39 18.85 50.07 14.58 64.65

20 2009 41.27 30.07 198 23.4 438 22 34.74 19.79 3.00 0.38 19.64 51.22 15.23 66.45

21 2010 51.52 27.94 202 24.6 710 49 33.87 19.81 2.82 0.43 19.76 53.55 22.33 75.88

22 2011 73.35 28.85 206 23.01 726 55 32.45 18.35 2.67 0.46 19.64 53.09 20.61 73.70

23 2012 76.78 28.85 211 22.14 653 34 32.79 17.80 2.76 0.41 19.91 52.63 18.88 71.51

24 2013 76.71 28.81 216 21.84 580 46 32.32 18.14 2.58 0.45 19.57 52.17 19.69 71.86

Appendix-5 Various input and output data: Rajasthan

201

Appendix-6 Various input and output data: Ramgarh

OUTPUT INPUT

S.

N. Year

Theoretical

Yield

Actual

Inflow

LULC

(Contribu

ting

effect)

Populat

ion

density

Water

Table

Rainf

all

Rainy

days >

5.0mm

Mean of

daily max.

temp.

Mean of

daily min.

temp.

Wind

speed

Relative

Humidity

Solar

radiation

Net

area

sown

Area

sown

more

than once

MCM MCM %

m mm days 0C 0C m/sec fraction MJ/m^2 % %

1 1983 51.37 55.45 30.37 228 15.46 654 40 31.98 16.93 2.44 0.44 18.55 50.66 19.22

2 1984 16.39 8.38 28.70 234 16.95 413 22 33.21 16.80 2.84 0.37 18.39 51.76 21.98

3 1985 36.22 40.47 27.03 241 13.98 566 27 33.45 17.22 2.80 0.40 19.02 52.86 24.73

4 1986 6.83 20.56 26.96 248 19.11 305 20 33.48 16.56 2.66 0.39 19.05 53.80 23.45

5 1987 2.29 4.56 26.90 254 16.83 210 11 34.83 17.38 2.58 0.34 18.26 54.74 22.17

6 1988 33.47 20.25 26.83 261 18.07 547 55 33.69 17.97 2.71 0.41 18.40 55.68 20.89

7 1989 21.14 0.91 26.77 267 18.88 454 23 33.30 16.51 2.69 0.37 18.89 56.62 19.61

8 1990 20.74 1.39 26.70 274 18.47 451 45 32.87 17.92 2.75 0.43 18.36 57.56 18.33

9 1991 6.67 4.79 26.63 281 23.45 302 21 33.52 17.35 2.56 0.39 18.19 58.50 17.05

10 1992 37.91 29.00 26.57 292 21.07 577 36 32.65 17.08 2.57 0.42 18.22 59.45 15.78

11 1993 42.93 15.72 26.50 303 24.69 606 35 33.51 18.16 2.69 0.39 18.31 59.33 16.37

12 1994 27.30 5.89 26.44 314 23.90 502 43 32.84 17.15 2.56 0.43 18.54 59.22 16.96

13 1995 95.23 49.67 26.37 325 25.17 856 44 32.83 17.57 2.55 0.42 18.39 60.63 17.54

14 1996 96.59 53.10 26.30 337 24.09 862 62 32.79 17.37 2.60 0.44 18.15 60.85 19.75

15 1997 32.64 27.78 26.24 348 23.93 541 38 31.39 16.91 2.58 0.46 17.32 61.06 21.96

16 1998 35.02 14.22 26.17 359 23.58 558 38 33.17 18.12 2.58 0.43 18.12 61.28 24.17

17 1999 20.17 8.07 26.11 370 24.16 446 19 35.77 18.75 2.75 0.34 19.14 62.95 26.4

18 2000 15.30 3.54 26.04 381 23.08 402 26 34.83 16.51 2.76 0.35 19.25 60.27 23.66

19 2001 11.65 1.93 25.97 393 24.39 364 25 34.49 16.89 2.53 0.36 19.10 52.84 20.88

20 2002 2.72 0.06 25.91 405 24.62 223 12 35.82 16.88 2.68 0.34 19.02 56.29 23.23

21 2003 112.33 7.34 25.84 416 25.30 922 58 33.22 17.18 2.46 0.42 19.03 46.64 15.02

22 2004 33.54 1.73 24.02 428 27.19 547 15 34.41 18.09 2.60 0.37 19.10 51.45 18.35

23 2005 95.24 7.31 22.20 439 29.59 856 19 33.60 16.69 2.55 0.38 18.51 56.25 21.67

24 2006 18.44 1.87 21.21 451 28.29 432 9 34.54 17.25 2.57 0.38 18.20 56.25 21.97

202

25 2007 16.24 0.85 20.22 462 29.46 412 23 34.09 17.84 2.60 0.39 17.74 56.25 22.27

26 2008 92.95 0.76 19.23 474 30.72 847 50 33.35 17.82 2.50 0.41 17.90 57.66 25.14

27 2009 11.41 0.00 18.24 485 33.61 361 27 34.46 18.48 2.68 0.38 18.59 59.06 28.01

28 2010 98.21 1.08 17.25 497 36.68 868 62 33.66 18.87 2.44 0.44 18.86 59.09 25.41

29 2011 64.33 0.00 16.26 508 39.63 721 20 32.99 17.85 2.70 0.39 18.92 60.13 31.45

30 2012 47.40 0.00 15.27 519 43.64 632 35 33.95 16.93 2.68 0.38 19.13 61.17 37.49

31 2013 59.37 0.00 14.28 531 43.62 696 38 32.80 17.72 2.66 0.42 18.43 62.05 36.85

32 2014 26.60 0.00 13.29 542 44.50 497 22 32.21 17.88 2.54 0.42 17.79 62.75 35.43

203

Appendix-7 Various input and output data: Bisalpur

OUTPUT INPUT

S.N. Year Theoretical

Yield

Actual

Inflow

LULC

(Contri-

buting

effect)

Population

density

Water

Table Rainfall

Rainy

days >

5.0mm

Mean of

daily

max.

temp.

Mean

of daily

min.

temp.

Wind

speed

Relative

Humidity

Solar

radiation

Net

area

sown

Area

sown

more

than

once

MCM MCM %

m mm days 0C 0C m/sec fraction MJ/m^2 % %

1 1979 1489.79 1233.93 45.59 216 10.50 737 23 33.47 19.14 2.95 0.39 19.20 31.07 10.77

2 1980 244.95 712.26 45.9 221 10.54 360 22 34.07 19.19 2.95 0.36 19.50 30.48 9.2

3 1981 339.28 476.64 45.08 226 10.58 408 32 33.03 19.04 2.95 0.42 18.96 31.15 9.7

4 1982 838.86 3592.47 44.26 231 10.62 585 24 32.87 19.05 2.87 0.42 18.63 31.82 10.2

5 1983 1504.39 2333.90 43.44 236 10.66 744 34 32.87 18.24 2.74 0.40 19.22 32.49 10.7

6 1984 571.51 2464.46 42.62 240 10.71 498 18 33.27 18.12 3.03 0.36 19.55 33.16 11.2

7 1985 295.38 1075.62 41.8 245 11.60 383 20 34.02 18.70 2.93 0.36 19.68 33.85 12.7

8 1986 623.62 4914.76 41.464 250 12.52 516 21 33.55 18.45 3.01 0.37 19.68 34.2 12.83

9 1987 245.82 1542.34 41.128 255 12.92 362 11 34.84 19.09 2.89 0.32 19.10 34.55 12.96

10 1988 770.32 661.29 40.792 260 14.32 564 52 33.82 19.35 2.92 0.40 18.89 34.9 13.09

11 1989 996.88 1375.81 40.456 264 14.03 627 29 33.31 18.42 2.94 0.36 19.22 35.25 13.22

12 1990 1492.21 2182.95 40.12 269 12.54 741 46 33.02 19.17 2.99 0.42 18.47 35.6 13.35

13 1991 742.00 1951.57 39.784 274 11.56 555 24 33.85 19.06 2.90 0.37 19.31 35.95 13.47

14 1992 1186.07 791.28 39.45 281 13.38 675 33 33.15 18.67 2.80 0.40 19.02 36.3 13.6

15 1993 376.38 810.82 38.61 287 12.14 425 28 33.80 19.48 2.99 0.38 18.87 36.72

3 14.67

16 1994 1779.10 2070.80 37.77 294 14.52 806 44 32.66 18.88 2.78 0.43 18.41 37.14

6 15.74

17 1995 677.71 442.93 36.94 300 12.34 534 31 33.38 19.22 2.87 0.39 19.00 37.57 16.82

18 1996 1766.64 3122.34 37.24 307 13.89 803 46 33.04 18.70 2.90 0.41 19.02 37.04 15.04

19 1997 882.75 323.99 37.54 314 11.25 596 43 32.12 18.61 3.06 0.46 18.28 36.51 13.26

20 1998 357.97 90.06 37.84 320 12.66 417 30 33.98 19.41 2.87 0.40 18.98 35.98 11.48

21 1999 617.11 537.24 38.14 327 14.99 514 18 35.54 20.14 3.09 0.34 19.73 35.45 9.7

22 2000 312.94 313.79 38.43 333 14.93 394 25 34.87 18.74 3.08 0.32 19.73 34.91 7.92

23 2001 963.18 1034.55 38.62 340 16.05 618 35 34.29 18.61 2.86 0.35 19.61 34.61 6.95

24 2002 195.13 16.99 38.80 346 15.30 336 20 35.45 19.56 3.19 0.31 20.00 34.31 5.98

25 2003 741.15 176.72 38.99 353 17.21 555 46 34.01 19.00 3.01 0.38 19.42 34.01 5.00

26 2004 1271.88 1767.49 36.19 359 16.56 696 19 34.63 19.22 2.88 0.35 19.50 35.88 8.95

27 2005 948.46 168.51 33.39 365 14.27 614 22 33.80 18.33 2.78 0.36 19.64 37.75 12.90

28 2006 1683.09 2011.89 30.58 372 15.59 786 23 34.10 19.26 2.74 0.39 18.90 39.62 16.85

204

29 2007 599.83 142.74 30.94 378 13.00 508 30 33.87 19.40 2.84 0.39 19.06 41.68 20.8

30 2008 778.25 161.43 31.3 384 15.16 567 40 33.69 18.94 2.89 0.38 18.90 40.89 17.1

31 2009 327.95 21.23 32.6 390 16.15 403 22 34.75 19.86 3.01 0.37 19.70 39.92 15.68

32 2010 949.87 234.67 33.9 397 17.76 615 48 33.89 19.86 2.84 0.43 19.80 39.26 9.11

33 2011 1244.41 804.29 32.44 403 15.87 689 57 32.42 18.37 2.66 0.46 19.68 40.72 15.29

34 2012 1224.30 576.32 30.97 409 15.30 685 34 32.73 17.84 2.76 0.41 19.95 42.17 21.47

35 2013 1465.00 803.88 29.51 416 14.41 738 46 32.29 18.17 2.58 0.45 19.63 35.96 12.58

205

Appendix-8a Cosine Amplitude Method (CAM): Rajasthan

X-1 X-2 X-3 X-4

Year x1 x2 x3 x4 y Y2 xy x2 xy x2 xy x2 xy x2

1990 0.92 0.75 0.69 0.27 0.73 0.53 0.68 0.85 0.55 0.57 0.50 0.47 0.20 0.07

1991 0.41 0.36 0.52 0.50 0.87 0.75 0.35 0.17 0.31 0.13 0.45 0.27 0.44 0.25

1992 0.77 0.59 0.36 0.30 0.64 0.41 0.50 0.60 0.38 0.35 0.23 0.13 0.19 0.09

1993 0.53 0.47 0.68 0.49 0.58 0.33 0.31 0.28 0.27 0.22 0.39 0.46 0.28 0.24

1994 0.87 0.78 0.32 0.17 1.00 1.00 0.87 0.76 0.78 0.60 0.32 0.10 0.17 0.03

1995 0.75 0.55 0.47 0.37 0.85 0.72 0.64 0.57 0.47 0.31 0.40 0.22 0.31 0.13

1996 1.00 0.68 0.53 0.27 0.83 0.70 0.83 1.00 0.57 0.46 0.44 0.28 0.23 0.07

1997 0.89 1.00 0.78 0.00 0.73 0.54 0.65 0.78 0.73 1.00 0.57 0.62 0.00 0.00

1998 0.65 0.57 0.48 0.54 0.49 0.24 0.32 0.42 0.28 0.32 0.24 0.23 0.26 0.29

1999 0.47 0.15 0.86 1.00 0.23 0.05 0.11 0.22 0.03 0.02 0.20 0.73 0.23 1.00

2000 0.24 0.02 0.83 0.80 0.20 0.04 0.05 0.06 0.00 0.00 0.17 0.69 0.16 0.65

2001 0.58 0.26 0.45 0.64 0.50 0.25 0.29 0.34 0.13 0.07 0.22 0.20 0.32 0.41

2002 0.00 0.00 1.00 0.98 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.95

2003 0.67 0.48 0.69 0.54 0.50 0.25 0.33 0.45 0.24 0.23 0.34 0.48 0.27 0.29

2004 0.53 0.26 0.49 0.73 0.73 0.53 0.39 0.28 0.19 0.07 0.36 0.24 0.54 0.54

2005 0.48 0.31 0.32 0.49 0.73 0.53 0.35 0.23 0.22 0.09 0.24 0.11 0.36 0.24

2006 0.88 0.48 0.25 0.59 0.96 0.92 0.85 0.78 0.46 0.23 0.24 0.06 0.57 0.35

2007 0.62 0.48 0.42 0.52 0.71 0.50 0.44 0.39 0.34 0.23 0.30 0.18 0.37 0.27

2008 0.66 0.49 0.51 0.46 0.46 0.21 0.31 0.44 0.22 0.24 0.23 0.26 0.21 0.21

2009 0.33 0.41 0.72 0.77 0.19 0.04 0.06 0.11 0.08 0.17 0.14 0.52 0.15 0.59

2010 0.88 0.78 0.41 0.52 0.38 0.14 0.33 0.77 0.29 0.60 0.15 0.17 0.19 0.27

2011 0.91 0.98 0.15 0.10 0.77 0.60 0.71 0.83 0.76 0.96 0.11 0.02 0.08 0.01

2012 0.77 0.63 0.31 0.20 0.84 0.70 0.64 0.59 0.53 0.40 0.26 0.09 0.17 0.04

2013 0.62 0.93 0.00 0.07 0.84 0.70 0.51 0.38 0.77 0.86 0.00 0.00 0.06 0.00

Sum= 10.70 10.52 11.30 8.62 8.14 6.52 7.53 5.75 7.00

120.99 87.14 80.59 74.98

Cosine Amplitude Method (CAM): Relative Influence Value (Rij)= 0.96 0.92 0.73 0.66

Where X1= Rainfall, X2= = Mean of Relative Humidity, X3= Mean of daily wind speed, X4= Mean of daily max. temp., Y= Output (Inflow %)

206

Appendix-8b Cosine Amplitude Method (CAM): Ramgarh

X-1 X-2 X-3 X-4 X-5

Year x1 x2 x3 x4 x5 y Y2 xy x2 xy x2 xy x2 xy x2 xy x2

1983 1.00 0.62 0.00 0.05 0.13 1.00 1.00 1.00 1.00 0.62 0.39 0.00 0.00 0.05 0.00 0.13 0.02

1984 0.90 0.29 0.02 0.10 0.41 0.15 0.02 0.14 0.81 0.04 0.08 0.00 0.00 0.01 0.01 0.06 0.17

1985 0.80 0.50 0.04 0.00 0.46 0.73 0.53 0.59 0.65 0.36 0.25 0.03 0.00 0.00 0.00 0.34 0.22

1986 0.80 0.13 0.06 0.17 0.47 0.37 0.14 0.30 0.64 0.05 0.02 0.02 0.00 0.06 0.03 0.18 0.22

1987 0.80 0.00 0.08 0.09 0.78 0.08 0.01 0.07 0.63 0.00 0.00 0.01 0.01 0.01 0.01 0.06 0.60

1988 0.79 0.47 0.10 0.13 0.52 0.37 0.13 0.29 0.63 0.17 0.22 0.04 0.01 0.05 0.02 0.19 0.27

1989 0.79 0.34 0.13 0.16 0.43 0.02 0.00 0.01 0.62 0.01 0.12 0.00 0.02 0.00 0.03 0.01 0.19

1990 0.79 0.34 0.15 0.15 0.33 0.03 0.00 0.02 0.62 0.01 0.11 0.00 0.02 0.00 0.02 0.01 0.11

1991 0.78 0.13 0.17 0.31 0.48 0.09 0.01 0.07 0.61 0.01 0.02 0.01 0.03 0.03 0.10 0.04 0.23

1992 0.78 0.52 0.20 0.23 0.28 0.52 0.27 0.41 0.60 0.27 0.27 0.11 0.04 0.12 0.05 0.15 0.08

1993 0.77 0.56 0.24 0.35 0.48 0.28 0.08 0.22 0.60 0.16 0.31 0.07 0.06 0.10 0.12 0.14 0.23

1994 0.77 0.41 0.27 0.32 0.33 0.11 0.01 0.08 0.59 0.04 0.17 0.03 0.08 0.03 0.11 0.03 0.11

1995 0.77 0.91 0.31 0.37 0.32 0.90 0.80 0.69 0.59 0.81 0.82 0.28 0.10 0.33 0.13 0.29 0.11

1996 0.76 0.92 0.35 0.33 0.32 0.96 0.92 0.73 0.58 0.88 0.84 0.33 0.12 0.32 0.11 0.30 0.10

1997 0.76 0.46 0.38 0.33 0.00 0.50 0.25 0.38 0.57 0.23 0.22 0.19 0.15 0.16 0.11 0.00 0.00

1998 0.75 0.49 0.42 0.31 0.40 0.26 0.07 0.19 0.57 0.13 0.24 0.11 0.17 0.08 0.10 0.10 0.16

1999 0.75 0.33 0.45 0.33 0.99 0.15 0.02 0.11 0.56 0.05 0.11 0.07 0.21 0.05 0.11 0.14 0.98

2000 0.75 0.27 0.49 0.30 0.78 0.06 0.00 0.05 0.56 0.02 0.07 0.03 0.24 0.02 0.09 0.05 0.60

2001 0.74 0.22 0.53 0.34 0.70 0.03 0.00 0.03 0.55 0.01 0.05 0.02 0.28 0.01 0.12 0.02 0.49

2002 0.74 0.02 0.56 0.35 1.00 0.00 0.00 0.00 0.55 0.00 0.00 0.00 0.32 0.00 0.12 0.00 1.00

2003 0.73 1.00 0.60 0.37 0.41 0.13 0.02 0.10 0.54 0.13 1.00 0.08 0.36 0.05 0.14 0.05 0.17

2004 0.63 0.47 0.64 0.43 0.68 0.03 0.00 0.02 0.39 0.01 0.22 0.02 0.40 0.01 0.19 0.02 0.47

2005 0.52 0.91 0.67 0.51 0.50 0.13 0.02 0.07 0.27 0.12 0.82 0.09 0.45 0.07 0.26 0.07 0.25

2006 0.46 0.31 0.71 0.47 0.71 0.03 0.00 0.02 0.22 0.01 0.10 0.02 0.50 0.02 0.22 0.02 0.51

2007 0.41 0.28 0.75 0.51 0.61 0.02 0.00 0.01 0.16 0.00 0.08 0.01 0.56 0.01 0.26 0.01 0.37

2008 0.35 0.89 0.78 0.55 0.44 0.01 0.00 0.00 0.12 0.01 0.80 0.01 0.61 0.01 0.30 0.01 0.20

2009 0.29 0.21 0.82 0.64 0.69 0.00 0.00 0.00 0.08 0.00 0.04 0.00 0.67 0.00 0.41 0.00 0.48

2010 0.23 0.92 0.86 0.74 0.51 0.02 0.00 0.00 0.05 0.02 0.85 0.02 0.73 0.01 0.55 0.01 0.26

2011 0.17 0.72 0.89 0.84 0.36 0.00 0.00 0.00 0.03 0.00 0.52 0.00 0.79 0.00 0.71 0.00 0.13

2012 0.12 0.59 0.93 0.97 0.58 0.00 0.00 0.00 0.01 0.00 0.35 0.00 0.86 0.00 0.94 0.00 0.33

2013 0.06 0.68 0.96 0.97 0.32 0.00 0.00 0.00 0.00 0.00 0.47 0.00 0.93 0.00 0.94 0.00 0.10

2014 0.00 0.40 1.00 1.00 0.19 0.00 0.00 0.00 0.00 0.00 0.16 0.00 1.00 0.00 1.00 0.00 0.03

sum 4.31 5.57 14.43 4.18 9.72 1.60 9.71 1.62 7.31 2.45 9.20

62.15

41.86

41.82

31.47

39.60

Cosine Amplitude Method (CAM): Relative Importance Value (Rij)= 0.71

0.65

0.25

0.29

0.39

Where X1= LULC (Contributing area %), X2= = Rainfall, X3= Population density, X4= GWL below GL, X5= Mean of daily max. temp., Y= Output (Inflow, MCM)

207

Appendix-8c Cosine Amplitude Method (CAM): Bisalpur

X-1 X-2 X-3 X-4

Year x1 x2 x3 x4 y Y2 xy x2 xy x2 xy x2 xy x2 1979 0.00 0.98 0.85 0.00 0.25 0.06 0.00 0.00 0.24 0.96 0.21 0.73 0.00 0.00

1980 0.02 1.00 0.05 0.01 0.14 0.02 0.00 0.00 0.14 1.00 0.01 0.00 0.00 0.00

1981 0.05 0.95 0.15 0.01 0.09 0.01 0.00 0.00 0.09 0.90 0.01 0.02 0.00 0.00

1982 0.07 0.90 0.53 0.02 0.73 0.53 0.05 0.01 0.66 0.81 0.39 0.28 0.01 0.00

1983 0.10 0.85 0.87 0.02 0.47 0.22 0.05 0.01 0.40 0.72 0.41 0.76 0.01 0.00

1984 0.12 0.80 0.34 0.03 0.50 0.25 0.06 0.01 0.40 0.64 0.17 0.12 0.01 0.00

1985 0.14 0.75 0.10 0.15 0.22 0.05 0.03 0.02 0.16 0.56 0.02 0.01 0.03 0.02

1986 0.17 0.73 0.38 0.28 1.00 1.00 0.17 0.03 0.73 0.53 0.38 0.15 0.28 0.08

1987 0.19 0.71 0.06 0.33 0.31 0.10 0.06 0.04 0.22 0.50 0.02 0.00 0.10 0.11

1988 0.22 0.69 0.49 0.53 0.13 0.02 0.03 0.05 0.09 0.47 0.06 0.24 0.07 0.28

1989 0.24 0.67 0.62 0.49 0.28 0.08 0.07 0.06 0.19 0.45 0.17 0.38 0.13 0.24

1990 0.27 0.65 0.86 0.28 0.44 0.20 0.12 0.07 0.29 0.42 0.38 0.74 0.12 0.08

1991 0.29 0.63 0.47 0.15 0.39 0.16 0.11 0.08 0.25 0.39 0.18 0.22 0.06 0.02

1992 0.32 0.61 0.72 0.40 0.16 0.02 0.05 0.10 0.10 0.37 0.11 0.52 0.06 0.16

1993 0.36 0.56 0.19 0.23 0.16 0.03 0.06 0.13 0.09 0.31 0.03 0.04 0.04 0.05

1994 0.39 0.50 1.00 0.55 0.42 0.18 0.16 0.15 0.21 0.25 0.42 1.00 0.23 0.31

1995 0.42 0.45 0.42 0.25 0.09 0.01 0.04 0.18 0.04 0.21 0.04 0.18 0.02 0.06

1996 0.45 0.47 0.99 0.47 0.63 0.40 0.29 0.21 0.30 0.22 0.63 0.99 0.30 0.22

1997 0.49 0.49 0.55 0.10 0.06 0.00 0.03 0.24 0.03 0.24 0.03 0.31 0.01 0.01

1998 0.52 0.51 0.17 0.30 0.01 0.00 0.01 0.27 0.01 0.26 0.00 0.03 0.00 0.09

1999 0.55 0.53 0.38 0.62 0.11 0.01 0.06 0.31 0.06 0.28 0.04 0.14 0.07 0.38

208

2000 0.59 0.54 0.12 0.61 0.06 0.00 0.04 0.34 0.03 0.30 0.01 0.02 0.04 0.37

2001 0.62 0.56 0.60 0.76 0.21 0.04 0.13 0.38 0.12 0.31 0.12 0.36 0.16 0.58

2002 0.65 0.57 0.00 0.66 0.00 0.00 0.00 0.43 0.00 0.32 0.00 0.00 0.00 0.44

2003 0.68 0.58 0.47 0.92 0.03 0.00 0.02 0.47 0.02 0.33 0.02 0.22 0.03 0.85

2004 0.72 0.41 0.77 0.83 0.36 0.13 0.26 0.51 0.15 0.17 0.27 0.59 0.30 0.70

2005 0.75 0.24 0.59 0.52 0.03 0.00 0.02 0.56 0.01 0.06 0.02 0.35 0.02 0.27

2006 0.78 0.07 0.96 0.70 0.41 0.17 0.32 0.61 0.03 0.00 0.39 0.92 0.29 0.49

2007 0.81 0.09 0.37 0.34 0.03 0.00 0.02 0.66 0.00 0.01 0.01 0.13 0.01 0.12

2008 0.84 0.11 0.49 0.64 0.03 0.00 0.02 0.71 0.00 0.01 0.01 0.24 0.02 0.41

2009 0.87 0.19 0.14 0.78 0.00 0.00 0.00 0.76 0.00 0.04 0.00 0.02 0.00 0.61

2010 0.91 0.27 0.59 1.00 0.04 0.00 0.04 0.82 0.01 0.07 0.03 0.35 0.04 1.00

2011 0.94 0.18 0.75 0.74 0.16 0.03 0.15 0.88 0.03 0.03 0.12 0.57 0.12 0.55

2012 0.97 0.09 0.74 0.66 0.11 0.01 0.11 0.94 0.01 0.01 0.08 0.55 0.08 0.44

2013 1.00 0.00 0.86 0.54 0.16 0.03 0.16 1.00 0.00 0.00 0.14 0.73 0.09 0.29

sum 3.75 2.74 11.02 5.09 12.16 4.96 11.91 2.74 9.22

41.32

45.58

44.65

34.56

Cosine Amplitude Method (CAM): Relative Importance Value

(Rij)=

0.43

0.75

0.74

0.47

Where X1= Population density, X2= LULC (Contributing area %), X3= = Rainfall, X4= GWL below GL, X5= Mean of daily max. temp., Y= Output (Inflow, MCM)

209

Appendix-9: Weighted rainfall in the catchment of Ramgarh Dam

S.

No. Year

Shahpura Amer Jamwa Ramgarh Virat Nagar Weighted

Rainfall

( mm) Monsoon

RF

I. F. Monsoon

RF

I. F. Monsoon

RF

I. F. Monsoon

RF

I. F.

0.49 0.08 0.30 0.13 1.00

1 1983 - - 685.6 107.5 666.9 392.3 605.0 154.2 654.1

2 1984 - - 486.9 102.5 393.9 311.0 - - 413.5

3 1985 - - 588.3 92.3 564.7 332.2 554.0 141.2 565.7

4 1986 - - 373.3 58.6 291.7 171.6 293.0 74.7 304.8

5 1987 - - 295.4 46.3 192.8 113.4 196.1 50.0 209.7

6 1988 - - 376.1 59.0 505.9 297.6 747.1 190.4 547.0

7 1989 - - 651.5 102.2 405.8 238.7 444.0 113.2 454.1

8 1990 - - 587.5 92.2 430.0 252.9 415.0 105.8 450.9

9 1991 196.5 96.3 495.0 39.6 440.5 132.2 265.0 34.5 302.5

10 1992 457.2 224.0 857.0 68.6 653.0 195.9 681.0 88.5 577.0

11 1993 640.0 313.6 809.0 64.7 468.4 140.5 672.0 87.4 606.2

12 1994 485.0 237.7 676.0 54.1 447.0 134.1 588.5 76.5 502.3

13 1995 832.0 407.7 963.0 77.0 870.9 261.3 847.8 110.2 856.2

14 1996 781.0 382.7 840.0 67.2 993.0 297.9 875.6 113.8 861.6

15 1997 544.0 266.6 524.9 42.0 495.0 148.5 648.0 84.2 541.3

16 1998 507.4 248.6 685.4 54.8 568.0 170.4 643.5 83.7 557.5

17 1999 444.0 217.6 354.0 28.3 445.0 133.5 514.9 66.9 446.3

18 2000 361.0 176.9 264.0 21.1 505.0 151.5 407.0 52.9 402.4

19 2001 373.0 182.8 335.0 26.8 312.5 93.8 464.5 60.4 363.7

20 2002 211.0 103.4 182.0 14.6 269.0 80.7 183.5 23.9 222.5

21 2003 902.0 442.0 631.0 50.5 996.0 298.8 1005.0 130.7 921.9

22 2004 505.0 247.5 628.0 50.2 697.0 209.1 313.0 40.7 547.5

23 2005 851.0 417.0 453.0 36.2 959.0 287.7 887.0 115.3 856.2

24 2006 469.0 229.8 316.0 25.3 422.0 126.6 387.0 50.3 432.0

25 2007 309.2 151.5 434.0 34.7 596.5 179.0 360.0 46.8 412.0

26 2008 751.4 368.2 469.0 37.5 1118.0 335.4 814.5 105.9 847.0

27 2009 300.0 147.0 241.0 19.3 521.0 156.3 296.0 38.5 361.1

28 2010 868.0 425.3 656.0 52.5 945.0 283.5 821.0 106.7 868.0

29 2011 831.0 407.2 539.0 43.1 541.0 162.3 830.0 107.9 720.5

30 2012 636.0 311.6 786.0 62.9 599.0 179.7 599.0 77.9 632.1

31 2013 615.0 301.4 854.0 68.3 684.0 205.2 931.0 121.0 695.9

32 2014 398.0 195.0 568.0 45.4 679.0 203.7 405.0 52.7 496.8

Min.

196.5

182.0

192.8

183.5

209.7

Max.

902.0

963.0

1118.0

1005.0

921.9

Avg.

552.8

550.2

583.6

570.8

550.9

210

Appendix-10: Groundwater resources of Rajasthan state as on 31.03.2011 (CGWB 2014) OE=Over Exploited, C= Critical, SC= Semi-Critical

Sl.

No.

Assessment

Unit/

District

Area Potential

Area

Total

Annual

Replenish

able

Water

Recharge

Natural

Discharge

during

Non -

Monsoon

season

Net

Annual

Ground

Water

Availabi

lity

Existing

Gross

Ground

Water Draft

for

irrigation

Existing Gross

Ground Water

Draft for

domestic and

industrial water

supply

Existing

Gross

Ground

Water

Draft for

All Uses

Net

Groundwater

Availability

for future

Irrigation

Development

Groundwater

Allocation for

Domestic &

Industrial

Development

Stage of

G.W.

Develop-

ment.

Categor

y

Sq. km Sq. km MCM MCM MCM MCM MCM MCM MCM MCM %

1 Ajmer 8481 7467 355.11 33.05 322.06 415.47 46.91 462.38 0 46.91 143.57 OE

2 Alwar 8720 6825 795.51 63.82 731.69 1215.52 95.66 1311.18 3.99 95.95 179.20 OE

3 Banswara 4536 3980 257.62 26.96 230.66 92.31 19.61 111.92 166.87 28.53 48.52 SAFE

4 Baran 6955 6892 519.67 48.04 471.63 525.96 39.38 565.34 55.62 44.75 119.87 OE

5 Barmer 28387 12735 272.53 25.24 247.29 239.01 67.66 306.67 21.17 68.47 124.01 OE

6 Bharatpur 5044 3413 492.5 43.14 449.36 458.39 63.78 522.17 3.96 65.69 116.20 OE

7 Bhilwara 10455 9355 464.32 44.76 419.56 501.98 37.96 539.94 0 39.8 128.69 OE

8 Bikaner 30382 13603 252.65 12.63 240.02 258.67 83.74 342.41 40.11 90.33 142.66 OE

9 Bundi 5500 4240 422.65 59.7 362.95 336.69 27.11 363.8 53.28 34.72 100.23 OE

10 Chittorgarh 7880 6095 352.76 34.17 318.59 432.27 13.79 446.06 0 13.79 140.01 OE

11 Churu 13793 5192 141.01 7.05 133.96 94.45 24.08 118.53 30.96 43.98 88.48 SC

12 Dausa 3420 3086 263.73 26.2 237.53 378.86 24.87 403.73 0 24.87 169.97 OE

13 Dholpur 3009 2486 283.12 23.82 259.3 307.06 25.26 332.32 15.96 25.76 128.16 OE

14 Dungarpur 3770 2634 141.03 12.91 128.12 83.96 8.82 92.78 0 44.16 72.42 SC

15 Ganganagar 11604 1546 407.98 40.8 367.18 156.17 5.45 161.62 200.68 10.33 44.02 SAFE

16 Hanumnagarh 9580 1279 226.49 22.65 203.84 157.16 7.05 164.21 34.46 11.07 80.56 SAFE

17 Jaipur 11061 9995 712.38 66.85 645.53 1081.11 256.86 1337.97 9.79 257.33 207.27 OE

18 Jaiselmer 38401 12090 67.92 6.33 61.59 93.56 28.93 122.49 18.74 29.61 198.88 OE

19 Jalore 10640 8228 462.85 40.86 421.99 779.52 40.9 820.42 9.69 41.91 194.42 OE

20 Jhalawar 6219 6106 441.71 29.48 412.23 476.01 15.83 491.84 4.79 19.4 119.31 OE

21 Jhunjhunu 5928 5274 263.51 23.5 240.01 456.04 85.71 541.75 3.3 86.04 225.72 OE

22 Jodhpur 22250 18868 429.52 42.39 387.13 727 103.97 830.97 47.61 113.03 214.65 OE

23 Karauli 5039 3902 373.14 36.17 336.97 411.77 50.64 462.41 12.39 49.84 137.23 OE

24 Kota 5204 5123 566.89 53.59 513.3 420.97 48.91 469.88 77.76 62.51 91.54 C

25 Nagaur 17718 16379 580.75 56.49 524.26 811.86 178.93 990.79 41.2 189.53 188.99 OE

26 Pali 12357 7363 328.59 32.29 296.3 314.36 27.47 341.83 9.59 30.48 115.37 OE

27 Pratapgarh 4360 2950 155.21 14.36 140.85 168.84 5.97 174.81 3.99 9.82 124.11 OE

28 Rajasmand 4635 3540 118.72 11.87 106.85 102.7 15.25 117.95 0.63 15.25 110.39 OE

29 Sawaimadhop

ur 5021 7263 321.48 30.37 291.11 370.52 58.42 428.94 2.09 83.75 147.35 OE

30 Sikar 7881 4326 396.36 35.94 360.42 374.08 79.47 453.55 9.72 62.38 125.84 OE

31 Sirohi 5136 4076 303.38 28.54 274.84 299.52 11.08 310.6 11.26 14.84 113.01 OE

32 Tonk 7200 6525 483.53 44.59 438.94 357.45 76.35 433.8 18.41 91.04 98.83 C

33 Udaipur 11761 7771 286.85 33.94 252.91 229.31 31.95 261.26 4.5 39.46 103.30 OE

STATE 342327 220604 11941.5 1112.49 10829 13133.182 1709.8153 14843 912.5 1885.32 137.07 OE

211

Appendix-10 (b): Abstractions in the catchment area of existing dams

and resulting in nearly nil inflow to these dams

212

S.N.50: Earthen Check Dam at Banjara

River/Drain Survey Measurement of Earthen Check Dam

Bore Well U/S of Earthen Check Dam GPS Instrument used for Co-ordinates

S.N.83: Earthen Check Dam at Milakpur

213

S.N.52: Showing Measurement and Extensive

Agriculture along Earthen Check Dam at Banjara S.N.53: Stone Mine at Nimbahedi

S.N.30: River at Milakpur Turk S.N.37: Earthen Check Dam at Bhaleshwar

Agriculture in River Bed Agriculture U/S of Earthen Check

Dam

214

Detail of earthen check dams/other WHS in the catchment of existing dams

S.N. Location Detail

Co-ordinates Approximate Capacity

Latitude Longitude Nos. L W H Quantity

(CUM)

Quantity

(mcft) 1 Johad, Jhundpuri 28005'36.70" 76050'12.2" 1 50 35 2.25 3937.5 0.139

2 deep excavation mitiya was 28004'44.95" 76050'45.05" 1 40 150 3 18000 0.636

3 deep excavation mitiya was 28004'44.71" 76050'47.28" 1 60 25 2.25 3375 0.119

4 deep excavation mitiya was 28004'52.9" 76050'54.7" 1 70 20 2 2800 0.099

5 old johad bubkahera 28004'42" 76051'22.4" 1 25 25 2 1250 0.044

6 old johad dholi pahadi 28004'3.9" 76052'59.6" 1 110 90 1.5 14850 0.524

7 old johad bhalesar 28004'13.9" 76054'1.7" 1 40 40 3 4800 0.169

8 E.C.D. ubaraka 28004'26.3" 76053'19.1" 1 100 20 4 8000 0.282

2 200 10 1 4000 0.141

9 E.C.D. ubaraka 28004'35.9" 76053'40.4" 1 55 30 4 6600 0.233

11 E.C.D. 1 150 10 4 6000 0.212

10 E.C.D. bhalesar 28004'36.2" 76053'50.1" 1 60 30 1.75 3150 0.111

11 E.C.D. ubaraka 28004'54" 76053'52.5" 1 100 20 3 6000 0.212

12 E.C.D. ubaraka 28004'52" 76053'56.6" 1 80 80 3.5 22400 0.791

1 200 25 2 10000 0.353

13 E.C.D. bhalesar 28004'47.2" 76054'7.4" 1 200 20 2.5 10000 0.353

14 E.C.D. bhalesar 28004'52.2" 76054'6.8" 1 150 30 2 9000 0.318

15 E.C.D. bhalesar 28004'47.9" 76054'1" 1 200 10 1.5 3000 0.106

16 E.C.D. ubaraka 28004'57.7" 76053'48.4" 1 30 25 2 1500 0.053

17 old johad ubaraka 28004'49.2" 76053'45.2" 1 150 150 2 45000 1.589

18 E.C.D. ubaraka 28005'2.5" 76053'37.7" 2 100 10 2 4000 0.141

19 E.C.D. ubaraka 28005'4.3" 76053'38.4" 1 200 20 3 12000 0.424

20 E.C.D. ubaraka 28005'5.5" 76053'42.9" 1 300 30 1 9000 0.318

21 E.C.D. gwalda 28005'10.2" 76053'47.3" 1 100 20 2 4000 0.141

1 300 20 2 12000 0.424

22 E.C.D. gwalda 28005'12.8" 76053'39.4" 1 40 20 3 2400 0.085

1 100 10 2 2000 0.071

23 E.C.D. ubaraka 28005'1.1" 76053'52.6" 1 40 20 1.5 1200 0.042

24 E.C.D. bhalesar 28004'53.8" 76054'14.9" 1 250 20 3 15000 0.530

25 E.C.D. bhalesar 28005'4.8" 76054'12.2" 1 120 20 2 4800 0.169

1 150 10 2 3000 0.106

1 60 30 3 5400 0.191

26 E.C.D. bhalesar 28004'39.7" 76054'44.2" 1 300 40 2 24000 0.847

27 E.C.D. bhalesar 28004'29.5" 76054'54.7" 2 200 10 3 12000 0.424

28 E.C.D. bhalesar 28004'28.7" 76054'51.6" 1 50 10 1.5 750 0.026

29 E.C.D. milakpur turk 28004'28.7" 76054'50.4" 1 100 10 2 2000 0.071

30 E.C.D. milakpur turk 28004'24.3" 76054'48" 1 100 10 2 2000 0.071




34 E.C.D. bhalesar 28004'33.2" 76055'2.5" 2 100 10 2 4000 0.141

35 E.C.D. gwalda 28005'31.7" 76053'44.8" 1 70 100 1.5 10500 0.371

36 old johad patla ki dhani gwalda 28005'49.3" 76053'9.1" 1 60 150 1 9000 0.318

37 E.C.D. gwalda patla ki dhani 28005'36.6" 76053'5.5" 1 60 30 3 5400 0.191

1 250 40 1 10000 0.353

38 E.C.D. gwalda 28005'27.2" 76053'6.4" 1 60 60 2.5 9000 0.318

1 150 20 1.5 4500 0.159

215

1 200 10 1.5 3000 0.106

39 E.C.D. gwalda 28005'29.3" 76053'13.4" 1 200 200 1.5 60000 2.119

40 E.C.D. gwalda supheda ki dhani 28005'17" 76054'16" 1 40 40 1 1600 0.056

41 E.C.D. gwalda supheda ki dhani 28005'12.1" 76054'15.5" 1 100 5 3 1500 0.053

1 40 30 3 3600 0.127

42 E.C.D. gwalda 28005'11.8" 76054'20.3" 1 100 10 4 4000 0.141

1 70 20 4 5600 0.198

43 E.C.D. gwalda ghamandi ki dhani 28005'9.2" 76054'26.6" 1 60 40 4 9600 0.339

1 150 20 3 9000 0.318

44 E.C.D. gwalda ghamandi ki dhani 28005'17.6" 76054'22.7" 1 30 10 3 900 0.032

1 20 10 3 600 0.021

45 E.C.D. banjhda 28005'24.9" 76054'35.9" 1 40 30 1.5 1800 0.064

1 150 10 1 1500 0.053

46 E.C.D. banjhda 28005'17.8" 76054'38.5" 1 60 30 4 7200 0.254

1 170 10 2 3400 0.120

47 E.C.D. banjhda 28005'13.7" 76054'44" 1 40 30 4 4800 0.169

1 200 20 3 12000 0.424

48 E.C.D. banjhda 28005'17.2" 76054'55.6" 1 50 30 4 6000 0.212

1 150 20 2 6000 0.212

1 200 20 2 8000 0.282

49 E.C.D. banjhda 28005'24.1" 76054'49.9" 1 60 10 3 1800 0.064

1 100 5 2 1000 0.035

1 20 10 2 400 0.014

50 deep excavated mines nimbahedi 28005'49.1" 76052'24" 1 200 100 15 300000 10.593

51 johad nakhnoal 28006'23.7" 76051'42.8" 1 60 40 1 2400 0.085

52 anicut nimbaheri 28006'0.1" 76051'53.1" 1 50 30 1.5 2250 0.079

53 Stone Mine nimbaheri 28005'59.8" 76052'3.4" 1 70 150 1.5 15750 0.556

54 E.C.D. nimbaheri 28005'59.4" 76052'11" 1 50 100 1.3 6500 0.230

55 anicut nimbaheri 28006'2.9" 76052'18.1" 1 80 20 1 1600 0.056

56 E.C.D. nimbaheri 28006'6.3" 76052'15.8" 1 50 10 1.5 750 0.026

2 100 10 1.5 3000 0.106

57 deep excavated mines, Nakh. 28005'57.3" 76052'0.6" 1 60 40 4 9600 0.339

58 johad milakpur turk 28003'35.4" 76053'38.9" 1 30 30 2 1800 0.064

59 E.C.D. bhalesar 28004'0.1" 76053'56.9" 1 50 5 1 250 0.009

60 E.C.D. bhalesar hyat ki dhani 28003'57.7" 76054'1.9" 1 40 10 2 800 0.028

1 50 5 1 250 0.009

61 E.C.D. Milakpur turk 28004'8.4" 76054'15.1" 1 150 40 2 12000 0.424

1 150 10 1 1500 0.053

62 E.C.D. milakpur turk chonchpuri 28004'8.7" 76054'41.5" 1 60 20 2 2400 0.085


1 200 15 2 6000 0.212

64 E.C.D. milakpur turk chonchpuri 28004'4" 76054'53.3" 1 100 10 2 2000 0.071

65 E.C.D. milakpur turk chonchpuri 28004'8.2" 76055'4.7" 2 100 10 1.5 3000 0.106

66 E.C.D. milakpur turk chonchpuri 28004'1.9" 76054'44.1" 2 50 5 1.5 750 0.026


1 50 5 2 500 0.018

68 E.C.D. milakpur turk chonchpuri 28004'4" 76054'30.4" 1 200 15 3 9000 0.318

69 E.C.D. milakpur turk 28003'33" 76053'55.3" 1 150 50 3.5 26250 0.927

1 300 40 2 24000 0.847


216

2 150 15 2 9000 0.318

71 E.C.D. milakpur turk 28003'33" 76054'42.5" 2 150 12 2 7200 0.254

72 E.C.D. milakpur turk 28003'30.8" 76054'40.2" 1 50 10 1.5 750 0.026




1 50 20 1.5 1500 0.053

76 E.C.D. milakpur turk 28003'10.7" 76054'24" 1 100 40 1.5 6000 0.212


1 150 20 2 6000 0.212

1 100 10 1 1000 0.035


2 100 5 1 1000 0.035

1 200 20 2 8000 0.282



2 100 5 2 2000 0.071






1 40 50 3 6000 0.212


1 300 50 1 15000 0.530








1 100 10 1 1000 0.035


1 200 25 1.5 7500 0.265

95 E.C.D. milakpur turk 28002'45.2" 76053'53" 1 100 50 2.5 12500 0.441

1 150 30 1 4500 0.159

2 100 10 1 2000 0.071


1 50 5 1 250 0.009



1 50 5 1.25 312.5 0.011

99 E.C.D. dholi pahadi 28004'14.5" 76053'4.6" 1 40 20 1 800 0.028

100 E.C.D. dholi pahadi 28004'16.2" 76052'53.7" 1 45 20 1.5 1350 0.048

1 50 15 1 750 0.026

101 E.C.D. patan khurd 28003'31.9" 76052'42.8" 1 30 20 20 12000 0.424

102 E.C.D. patan khurd 28003'35.5" 76052'15.7" 1 100 40 0.75 3000 0.106

103 E.C.D. patan khurd 28003'32.9" 76052'17.1" 1 125 20 1.5 3750 0.132

104 E.C.D. dholi pahadi 28004'4.7" 76052'7.2" 1 300 50 2 30000 1.059

TOTAL 45.203

E.C.D.= Earthen Check Dam

217

Appendix-11 Some actual site photographs showing the changes in LULC in the

catchment areas of dams

„(a) (b)

„(c) (d)

„(e) (f)

Figure showing several of abstractions within the catchment of dams (a) Agricultural

activity (b) Infrastructural development: road (c) Poor drainage at developed area:

obstructing the flow of water to natural drainage (d & e) Forest development activities:

CVH & DCB (f) Small water harvesting structures within the catchment of dams.

218

Some actual site photographs showing the stringent actions taken: Demolition of

residential buildings constructed within the drainage area of the natural stream.

Building situated at Shyam Nagar, Janpath at Jaipur was razed to the ground using the

blasting technique, alleged to be constructed in the drainage boundary of a natural stream.

Three multi-storied under construction buildings, one of them demolished by the blast

with 50 kg explosion ordered to be demolished for flouting building norms at Amanishah

Nullah, a tributary of a natural river, in Jaipur.

219

AUTHOR’S BIO-DATA

The author obtained his Bachelor‟s degree in Civil Engineering from MBM Engineering

College, Jodhpur in the year 1995 and Master‟s degree in Structural Engineering from

MNIT, Jaipur in 2011. Author has also obtained Master‟s degree in Business

Administration (MBA) from Indira Gandhi National Open University, New Delhi in the

year 2004. Author has been working as a water resources engineer since 1996. The author

has been designated as Chartered Engineer (India) by Institution of Engineers (India),

Kolkata. The author is presently working as Executive Engineer, Water Resources

Department of Government of Rajasthan. Previously, the author has worked as Assistant

Engineer and Junior Engineer in various engineering departments of Government of

Rajasthan. The author is also in the expert panel of Engineering Staff Training Institute,

Jaipur, which imparts training to the engineers of various government departments.

List of publications are as follows:

1. Gupta N.K., Jethoo A.S., and Gupta S.K. (2016). “Rainfall and Surface Water

Resources of Rajasthan State,” Water Policy Journal. IWA Publishing, Volume

18(2016), pp. 276-287.

2. Gupta, S.K., Sharma, G., Jethoo, A.S., Tyagi, J., Gupta, N.K. (2015). “A Critical

Review of Hydrological Models.” HYDRO 2015 International, IIT Roorkee, India,

17-19 December 2015, 20th International Conference on Hydraulics, Water

Resources and River Engineering

3. Gupta, S.K., Jethoo, A.S., Tyagi, J., Gupta, N.K., and Gautam, P.(2015).

“Application of Hydrological Models in Water Resources: A Review.”

International Journal of Computer &Mathematical Sciences, Vol. 4 (Sp.), March

2015, pp. 94-100.

4. Jethoo, A.S., Sharma, G., Gupta, N.K., and Gupta, S.K. (2015). “Temporal

Variation of Water Inflow to Bisalpur Dam- A threat to Water Management of

The Rajasthan State.” All India Symposium on Golden Jubilee, Civil Engineering

Department, MNIT, Jaipur

5. Gupta, N.K., and Jethoo, A.S. (2014). “Declining Trend of Water Inflow to the

dams of Rajasthan State.” International Journal of Technology, IJTech 2014 (1),

pp.14-21.

6. Gupta, N.K., Mathur, P., Jethoo, A.S. (2014). “Feasibility Analysis for

Construction of New Dams in Rajasthan State.” Proceedings of International

Symposium on Dams in Global Environmental Challenges, International

Commission on Large Dams (ICOLD), Bali, Indonesia, June 1th-6th, 2014.

7. Gupta, N.K., Jethoo, A.S., and Pandel, B., (2014). “Declining Trend of Water

220

Inflow in Bisalpur Dam: A Threat to Environmental Sustainability.” National

Seminar on Technological Advances & Their Impact on Environment, April 22,

2014 (NSTAIE-2014/36).

8. Gupta, N.K., Gupta, S.K., and Jethoo, A.S. (2014). “Hydrological Modeling of

Ramgarh Dam of Rajasthan State Using Soil and Water Assessment Tool

(SWAT).” Proceedings of 4th International Conference on Hydrology and

Watershed Management, Centre for Water Resources, Institute of Science and

Technology, JNTU, Telangana State, India, PP. 985-995.

9. Gupta, N.K., and Jethoo, A.S. (2013). “Helpline for Water Leakages- A Solution

to the Water Crisis.”Proceedings India Water Week-2013, Efficient Water

Management: Challenges and Opportunities Ministry of Water Resources,

Government of India, 8-12 April 2013, PP. 190

Research Papers under Review:

1. Gupta, N.K., and Jethoo, A.S. “Transboundary Management of Surplus Water in a

River Basin -A Case Study of Sabarmati River Basin in Rajasthan.” J. of

Institution of Engineers (India): Series A, Springer.

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